diff --git a/.gitignore b/.gitignore index 787d13ec67..d62ff35ac7 100644 --- a/.gitignore +++ b/.gitignore @@ -118,3 +118,5 @@ mmseg/.mim # Pytorch *.pth + +*.png \ No newline at end of file diff --git a/configs/_base_/datasets/ade20k.py b/configs/_base_/datasets/ade20k.py index 48340d11ee..926f2e5895 100644 --- a/configs/_base_/datasets/ade20k.py +++ b/configs/_base_/datasets/ade20k.py @@ -1,6 +1,6 @@ # dataset settings dataset_type = 'ADE20KDataset' -data_root = 'data/ade/ADEChallengeData2016' +data_root = "/media/ids/Ubuntu files/data/ADEChallengeData2016/" crop_size = (512, 512) train_pipeline = [ dict(type='LoadImageFromFile'), diff --git a/configs/_base_/datasets/arid10_cat .py b/configs/_base_/datasets/arid10_cat .py new file mode 100644 index 0000000000..52d02a4233 --- /dev/null +++ b/configs/_base_/datasets/arid10_cat .py @@ -0,0 +1,73 @@ +dataset_type = "arid10CATDataset" +data_root = "/media/ids/Ubuntu files/data/arid10_CAT/" +crop_size = (640, 480) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + type='RandomResize', + scale=(2048, 480), + ratio_range=(0.5, 2.0), + keep_ratio=True), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PackSegInputs') +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', scale=(2048, 480), keep_ratio=True), + # add loading annotation after ``Resize`` because ground truth + # does not need to do resize data transform + dict(type='LoadAnnotations'), + dict(type='PackSegInputs') +] +img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] +tta_pipeline = [ + dict(type='LoadImageFromFile', backend_args=None), + dict( + type='TestTimeAug', + transforms=[ + [ + dict(type='Resize', scale_factor=r, keep_ratio=True) + for r in img_ratios + ], + [ + dict(type='RandomFlip', prob=0., direction='horizontal'), + dict(type='RandomFlip', prob=1., direction='horizontal') + ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')] + ]) +] +train_dataloader = dict( + batch_size=2, + num_workers=2, + persistent_workers=True, + sampler=dict(type='InfiniteSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + pipeline=train_pipeline)) +val_dataloader = dict( + batch_size=1, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + pipeline=test_pipeline)) +test_dataloader = dict( + batch_size=1, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + pipeline=test_pipeline)) + +val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) +test_evaluator = val_evaluator diff --git a/configs/_base_/datasets/arid20_cat.py b/configs/_base_/datasets/arid20_cat.py new file mode 100644 index 0000000000..c30451f8a3 --- /dev/null +++ b/configs/_base_/datasets/arid20_cat.py @@ -0,0 +1,73 @@ +dataset_type = "ARID20CATDataset" +data_root = "/media/ids/Ubuntu files/data/ARID20_CAT/" +crop_size = (640, 480) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + type='RandomResize', + scale=(2048, 480), + ratio_range=(0.5, 2.0), + keep_ratio=True), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PackSegInputs') +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', scale=(2048, 480), keep_ratio=True), + # add loading annotation after ``Resize`` because ground truth + # does not need to do resize data transform + dict(type='LoadAnnotations'), + dict(type='PackSegInputs') +] +img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] +tta_pipeline = [ + dict(type='LoadImageFromFile', backend_args=None), + dict( + type='TestTimeAug', + transforms=[ + [ + dict(type='Resize', scale_factor=r, keep_ratio=True) + for r in img_ratios + ], + [ + dict(type='RandomFlip', prob=0., direction='horizontal'), + dict(type='RandomFlip', prob=1., direction='horizontal') + ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')] + ]) +] +train_dataloader = dict( + batch_size=2, + num_workers=2, + persistent_workers=True, + sampler=dict(type='InfiniteSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + pipeline=train_pipeline)) +val_dataloader = dict( + batch_size=1, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + pipeline=test_pipeline)) +test_dataloader = dict( + batch_size=1, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + pipeline=test_pipeline)) + +val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) +test_evaluator = val_evaluator diff --git a/configs/_base_/datasets/hots_v1_640x480.py b/configs/_base_/datasets/hots_v1_640x480.py new file mode 100644 index 0000000000..4d74705ad1 --- /dev/null +++ b/configs/_base_/datasets/hots_v1_640x480.py @@ -0,0 +1,73 @@ +dataset_type = "HOTSDataset" +data_root = "/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/" +crop_size = (640, 480) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + type='RandomResize', + scale=(2048, 480), + ratio_range=(0.5, 2.0), + keep_ratio=True), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PackSegInputs') +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', scale=(2048, 480), keep_ratio=True), + # add loading annotation after ``Resize`` because ground truth + # does not need to do resize data transform + dict(type='LoadAnnotations'), + dict(type='PackSegInputs') +] +img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] +tta_pipeline = [ + dict(type='LoadImageFromFile', backend_args=None), + dict( + type='TestTimeAug', + transforms=[ + [ + dict(type='Resize', scale_factor=r, keep_ratio=True) + for r in img_ratios + ], + [ + dict(type='RandomFlip', prob=0., direction='horizontal'), + dict(type='RandomFlip', prob=1., direction='horizontal') + ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')] + ]) +] +train_dataloader = dict( + batch_size=2, + num_workers=2, + persistent_workers=True, + sampler=dict(type='InfiniteSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + pipeline=train_pipeline)) +val_dataloader = dict( + batch_size=1, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + pipeline=test_pipeline)) +test_dataloader = dict( + batch_size=1, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + pipeline=test_pipeline)) + +val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) +test_evaluator = val_evaluator diff --git a/configs/_base_/datasets/hots_v1_cat.py b/configs/_base_/datasets/hots_v1_cat.py new file mode 100644 index 0000000000..e53ed6e9c8 --- /dev/null +++ b/configs/_base_/datasets/hots_v1_cat.py @@ -0,0 +1,73 @@ +dataset_type = "HOTSCATDataset" +data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/" +crop_size = (640, 480) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + type='RandomResize', + scale=(2048, 480), + ratio_range=(0.5, 2.0), + keep_ratio=True), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PackSegInputs') +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', scale=(2048, 480), keep_ratio=True), + # add loading annotation after ``Resize`` because ground truth + # does not need to do resize data transform + dict(type='LoadAnnotations'), + dict(type='PackSegInputs') +] +img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] +tta_pipeline = [ + dict(type='LoadImageFromFile', backend_args=None), + dict( + type='TestTimeAug', + transforms=[ + [ + dict(type='Resize', scale_factor=r, keep_ratio=True) + for r in img_ratios + ], + [ + dict(type='RandomFlip', prob=0., direction='horizontal'), + dict(type='RandomFlip', prob=1., direction='horizontal') + ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')] + ]) +] +train_dataloader = dict( + batch_size=2, + num_workers=2, + persistent_workers=True, + sampler=dict(type='InfiniteSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + pipeline=train_pipeline)) +val_dataloader = dict( + batch_size=1, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + pipeline=test_pipeline)) +test_dataloader = dict( + batch_size=1, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + pipeline=test_pipeline)) + +val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) +test_evaluator = val_evaluator diff --git a/configs/_base_/datasets/irl_vision_sim_512x512.py b/configs/_base_/datasets/irl_vision_sim_512x512.py new file mode 100644 index 0000000000..b69668a956 --- /dev/null +++ b/configs/_base_/datasets/irl_vision_sim_512x512.py @@ -0,0 +1,73 @@ +dataset_type = "IRLVisionSimDataset" +data_root = "/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/" +crop_size = (512, 512) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + type='RandomResize', + scale=(2048, 512), + ratio_range=(0.5, 2.0), + keep_ratio=True), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PackSegInputs') +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', scale=(2048, 512), keep_ratio=True), + # add loading annotation after ``Resize`` because ground truth + # does not need to do resize data transform + dict(type='LoadAnnotations'), + dict(type='PackSegInputs') +] +img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] +tta_pipeline = [ + dict(type='LoadImageFromFile', backend_args=None), + dict( + type='TestTimeAug', + transforms=[ + [ + dict(type='Resize', scale_factor=r, keep_ratio=True) + for r in img_ratios + ], + [ + dict(type='RandomFlip', prob=0., direction='horizontal'), + dict(type='RandomFlip', prob=1., direction='horizontal') + ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')] + ]) +] +train_dataloader = dict( + batch_size=2, + num_workers=2, + persistent_workers=True, + sampler=dict(type='InfiniteSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + pipeline=train_pipeline)) +val_dataloader = dict( + batch_size=1, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + pipeline=test_pipeline)) +test_dataloader = dict( + batch_size=1, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + pipeline=test_pipeline)) + +val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) +test_evaluator = val_evaluator diff --git a/configs/_base_/datasets/irl_vision_sim_640x480.py b/configs/_base_/datasets/irl_vision_sim_640x480.py new file mode 100644 index 0000000000..b58e48d524 --- /dev/null +++ b/configs/_base_/datasets/irl_vision_sim_640x480.py @@ -0,0 +1,73 @@ +dataset_type = "IRLVisionSimDataset" +data_root = "/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/" +crop_size = (640, 480) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + type='RandomResize', + scale=(2048, 480), + ratio_range=(0.5, 2.0), + keep_ratio=True), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PackSegInputs') +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', scale=(2048, 480), keep_ratio=True), + # add loading annotation after ``Resize`` because ground truth + # does not need to do resize data transform + dict(type='LoadAnnotations'), + dict(type='PackSegInputs') +] +img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] +tta_pipeline = [ + dict(type='LoadImageFromFile', backend_args=None), + dict( + type='TestTimeAug', + transforms=[ + [ + dict(type='Resize', scale_factor=r, keep_ratio=True) + for r in img_ratios + ], + [ + dict(type='RandomFlip', prob=0., direction='horizontal'), + dict(type='RandomFlip', prob=1., direction='horizontal') + ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')] + ]) +] +train_dataloader = dict( + batch_size=2, + num_workers=2, + persistent_workers=True, + sampler=dict(type='InfiniteSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + pipeline=train_pipeline)) +val_dataloader = dict( + batch_size=1, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + pipeline=test_pipeline)) +test_dataloader = dict( + batch_size=1, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + pipeline=test_pipeline)) + +val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) +test_evaluator = val_evaluator diff --git a/configs/_base_/datasets/irl_vision_sim_cat.py b/configs/_base_/datasets/irl_vision_sim_cat.py new file mode 100644 index 0000000000..b05f4874df --- /dev/null +++ b/configs/_base_/datasets/irl_vision_sim_cat.py @@ -0,0 +1,73 @@ +dataset_type = "IRLVisionSimCATDataset" +data_root = "/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/" +crop_size = (640, 480) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + type='RandomResize', + scale=(2048, 480), + ratio_range=(0.5, 2.0), + keep_ratio=True), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PackSegInputs') +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', scale=(2048, 480), keep_ratio=True), + # add loading annotation after ``Resize`` because ground truth + # does not need to do resize data transform + dict(type='LoadAnnotations'), + dict(type='PackSegInputs') +] +img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] +tta_pipeline = [ + dict(type='LoadImageFromFile', backend_args=None), + dict( + type='TestTimeAug', + transforms=[ + [ + dict(type='Resize', scale_factor=r, keep_ratio=True) + for r in img_ratios + ], + [ + dict(type='RandomFlip', prob=0., direction='horizontal'), + dict(type='RandomFlip', prob=1., direction='horizontal') + ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')] + ]) +] +train_dataloader = dict( + batch_size=2, + num_workers=2, + persistent_workers=True, + sampler=dict(type='InfiniteSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + pipeline=train_pipeline)) +val_dataloader = dict( + batch_size=1, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + pipeline=test_pipeline)) +test_dataloader = dict( + batch_size=1, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + pipeline=test_pipeline)) + +val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) +test_evaluator = val_evaluator diff --git a/configs/_base_/datasets/sodhots_c.py b/configs/_base_/datasets/sodhots_c.py new file mode 100644 index 0000000000..3d2c5fd01b --- /dev/null +++ b/configs/_base_/datasets/sodhots_c.py @@ -0,0 +1,73 @@ +dataset_type = "SODHOTSCDataset" +data_root = "/media/ids/Ubuntu files/data/SODHOTS-C/" +crop_size = (640, 480) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + type='RandomResize', + scale=(2048, 480), + ratio_range=(0.5, 2.0), + keep_ratio=True), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PackSegInputs') +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', scale=(2048, 480), keep_ratio=True), + # add loading annotation after ``Resize`` because ground truth + # does not need to do resize data transform + dict(type='LoadAnnotations'), + dict(type='PackSegInputs') +] +img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] +tta_pipeline = [ + dict(type='LoadImageFromFile', backend_args=None), + dict( + type='TestTimeAug', + transforms=[ + [ + dict(type='Resize', scale_factor=r, keep_ratio=True) + for r in img_ratios + ], + [ + dict(type='RandomFlip', prob=0., direction='horizontal'), + dict(type='RandomFlip', prob=1., direction='horizontal') + ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')] + ]) +] +train_dataloader = dict( + batch_size=2, + num_workers=2, + persistent_workers=True, + sampler=dict(type='InfiniteSampler', shuffle=True), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + pipeline=train_pipeline)) +val_dataloader = dict( + batch_size=1, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + pipeline=test_pipeline)) +test_dataloader = dict( + batch_size=1, + num_workers=2, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=dict( + type=dataset_type, + data_root=data_root, + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + pipeline=test_pipeline)) + +val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) +test_evaluator = val_evaluator diff --git a/configs/_base_/models/dpt_vit-b16.py b/configs/_base_/models/dpt_vit-b16.py index 90845b37b5..068a91b035 100644 --- a/configs/_base_/models/dpt_vit-b16.py +++ b/configs/_base_/models/dpt_vit-b16.py @@ -9,7 +9,7 @@ model = dict( type='EncoderDecoder', data_preprocessor=data_preprocessor, - pretrained='pretrain/vit-b16_p16_224-80ecf9dd.pth', # noqa + # pretrained='pretrain/vit-b16_p16_224-80ecf9dd.pth', # noqa backbone=dict( type='VisionTransformer', img_size=224, diff --git a/configs/_base_/models/fast_scnn.py b/configs/_base_/models/fast_scnn.py index 11127b0115..8616e958a1 100644 --- a/configs/_base_/models/fast_scnn.py +++ b/configs/_base_/models/fast_scnn.py @@ -33,7 +33,8 @@ norm_cfg=norm_cfg, align_corners=False, loss_decode=dict( - type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1)), + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1) + ), auxiliary_head=[ dict( type='FCNHead', diff --git a/configs/_base_/models/upernet_vit-b16_ln_mln.py b/configs/_base_/models/upernet_vit-b16_ln_mln.py index 776525ad98..02db32e6e4 100644 --- a/configs/_base_/models/upernet_vit-b16_ln_mln.py +++ b/configs/_base_/models/upernet_vit-b16_ln_mln.py @@ -10,7 +10,7 @@ model = dict( type='EncoderDecoder', data_preprocessor=data_preprocessor, - pretrained='pretrain/jx_vit_base_p16_224-80ecf9dd.pth', + # pretrained='pretrain/jx_vit_base_p16_224-80ecf9dd.pth', backbone=dict( type='VisionTransformer', img_size=(512, 512), diff --git a/configs/_base_/schedules/schedule_10k.py b/configs/_base_/schedules/schedule_10k.py new file mode 100644 index 0000000000..eb86969a99 --- /dev/null +++ b/configs/_base_/schedules/schedule_10k.py @@ -0,0 +1,24 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer, clip_grad=None) +# learning policy +param_scheduler = [ + dict( + type='PolyLR', + eta_min=1e-4, + power=0.9, + begin=0, + end=10000, + by_epoch=False) +] +# training schedule for 10k +train_cfg = dict(type='IterBasedTrainLoop', max_iters=10000, val_interval=1000) +val_cfg = dict(type='ValLoop') +test_cfg = dict(type='TestLoop') +default_hooks = dict( + timer=dict(type='IterTimerHook'), + logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), + param_scheduler=dict(type='ParamSchedulerHook'), + checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=1000), + sampler_seed=dict(type='DistSamplerSeedHook'), + visualization=dict(type='SegVisualizationHook')) diff --git a/configs/_base_/schedules/schedule_1k.py b/configs/_base_/schedules/schedule_1k.py new file mode 100644 index 0000000000..1feda05e7a --- /dev/null +++ b/configs/_base_/schedules/schedule_1k.py @@ -0,0 +1,24 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer, clip_grad=None) +# learning policy +param_scheduler = [ + dict( + type='PolyLR', + eta_min=1e-4, + power=0.9, + begin=0, + end=1000, + by_epoch=False) +] +# training schedule for 1k +train_cfg = dict(type='IterBasedTrainLoop', max_iters=1000, val_interval=200) +val_cfg = dict(type='ValLoop') +test_cfg = dict(type='TestLoop') +default_hooks = dict( + timer=dict(type='IterTimerHook'), + logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), + param_scheduler=dict(type='ParamSchedulerHook'), + checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=200), + sampler_seed=dict(type='DistSamplerSeedHook'), + visualization=dict(type='SegVisualizationHook')) diff --git a/configs/_base_/schedules/schedule_5k.py b/configs/_base_/schedules/schedule_5k.py new file mode 100644 index 0000000000..6c8cbc7b00 --- /dev/null +++ b/configs/_base_/schedules/schedule_5k.py @@ -0,0 +1,24 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer, clip_grad=None) +# learning policy +param_scheduler = [ + dict( + type='PolyLR', + eta_min=1e-4, + power=0.9, + begin=0, + end=5000, + by_epoch=False) +] +# training schedule for 5k +train_cfg = dict(type='IterBasedTrainLoop', max_iters=5000, val_interval=1000) +val_cfg = dict(type='ValLoop') +test_cfg = dict(type='TestLoop') +default_hooks = dict( + timer=dict(type='IterTimerHook'), + logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), + param_scheduler=dict(type='ParamSchedulerHook'), + checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=1000), + sampler_seed=dict(type='DistSamplerSeedHook'), + visualization=dict(type='SegVisualizationHook')) diff --git a/configs/convnext/convnext-tiny_upernet_8xb2-amp-10k_HOTS_v1-640x480.py b/configs/convnext/convnext-tiny_upernet_8xb2-amp-10k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..11a5e6f298 --- /dev/null +++ b/configs/convnext/convnext-tiny_upernet_8xb2-amp-10k_HOTS_v1-640x480.py @@ -0,0 +1,54 @@ +_base_ = [ + '../_base_/models/upernet_convnext.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_10k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth' # noqa +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict( + type='mmpretrain.ConvNeXt', + arch='tiny', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + decode_head=dict( + in_channels=[96, 192, 384, 768], + num_classes=46, + ), + auxiliary_head=dict(in_channels=384, num_classes=46), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)), +) + +optim_wrapper = dict( + _delete_=True, + type='AmpOptimWrapper', + optimizer=dict( + type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05), + paramwise_cfg={ + 'decay_rate': 0.9, + 'decay_type': 'stage_wise', + 'num_layers': 6 + }, + constructor='LearningRateDecayOptimizerConstructor', + loss_scale='dynamic') + +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + power=1.0, + begin=1500, + end=10000, + eta_min=0.0, + by_epoch=False, + ) +] + + diff --git a/configs/convnext/convnext-tiny_upernet_8xb2-amp-10k_HOTS_v1-640x480_pretrained_ade20k.py b/configs/convnext/convnext-tiny_upernet_8xb2-amp-10k_HOTS_v1-640x480_pretrained_ade20k.py new file mode 100644 index 0000000000..21c4f7c7f2 --- /dev/null +++ b/configs/convnext/convnext-tiny_upernet_8xb2-amp-10k_HOTS_v1-640x480_pretrained_ade20k.py @@ -0,0 +1,55 @@ +_base_ = [ + '../_base_/models/upernet_convnext.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_10k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth" +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth' # noqa +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict( + type='mmpretrain.ConvNeXt', + arch='tiny', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + decode_head=dict( + in_channels=[96, 192, 384, 768], + num_classes=46, + ), + auxiliary_head=dict(in_channels=384, num_classes=46), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)), +) + +optim_wrapper = dict( + _delete_=True, + type='AmpOptimWrapper', + optimizer=dict( + type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05), + paramwise_cfg={ + 'decay_rate': 0.9, + 'decay_type': 'stage_wise', + 'num_layers': 6 + }, + constructor='LearningRateDecayOptimizerConstructor', + loss_scale='dynamic') + +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + power=1.0, + begin=1500, + end=10000, + eta_min=0.0, + by_epoch=False, + ) +] + + diff --git a/configs/convnext/convnext-tiny_upernet_8xb2-amp-160k_HOTS_v1-640x480.py b/configs/convnext/convnext-tiny_upernet_8xb2-amp-160k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..4a5bc7a02c --- /dev/null +++ b/configs/convnext/convnext-tiny_upernet_8xb2-amp-160k_HOTS_v1-640x480.py @@ -0,0 +1,54 @@ +_base_ = [ + '../_base_/models/upernet_convnext.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth' # noqa +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict( + type='mmpretrain.ConvNeXt', + arch='tiny', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + decode_head=dict( + in_channels=[96, 192, 384, 768], + num_classes=46, + ), + auxiliary_head=dict(in_channels=384, num_classes=46), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)), +) + +optim_wrapper = dict( + _delete_=True, + type='AmpOptimWrapper', + optimizer=dict( + type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05), + paramwise_cfg={ + 'decay_rate': 0.9, + 'decay_type': 'stage_wise', + 'num_layers': 6 + }, + constructor='LearningRateDecayOptimizerConstructor', + loss_scale='dynamic') + +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + power=1.0, + begin=1500, + end=160000, + eta_min=0.0, + by_epoch=False, + ) +] + + diff --git a/configs/convnext/convnext-tiny_upernet_8xb2-amp-160k_HOTS_v1-640x480_pretrained_ade20k.py b/configs/convnext/convnext-tiny_upernet_8xb2-amp-160k_HOTS_v1-640x480_pretrained_ade20k.py new file mode 100644 index 0000000000..9fd5d74390 --- /dev/null +++ b/configs/convnext/convnext-tiny_upernet_8xb2-amp-160k_HOTS_v1-640x480_pretrained_ade20k.py @@ -0,0 +1,55 @@ +_base_ = [ + '../_base_/models/upernet_convnext.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth' # noqa +load_from = "checkpoints/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth" +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict( + type='mmpretrain.ConvNeXt', + arch='tiny', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + decode_head=dict( + in_channels=[96, 192, 384, 768], + num_classes=46, + ), + auxiliary_head=dict(in_channels=384, num_classes=46), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)), +) + +optim_wrapper = dict( + _delete_=True, + type='AmpOptimWrapper', + optimizer=dict( + type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05), + paramwise_cfg={ + 'decay_rate': 0.9, + 'decay_type': 'stage_wise', + 'num_layers': 6 + }, + constructor='LearningRateDecayOptimizerConstructor', + loss_scale='dynamic') + +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + power=1.0, + begin=1500, + end=160000, + eta_min=0.0, + by_epoch=False, + ) +] + + diff --git a/configs/convnext/convnext-tiny_upernet_8xb2-amp-1k_HOTS_v1-640x480.py b/configs/convnext/convnext-tiny_upernet_8xb2-amp-1k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..3dd45fe36a --- /dev/null +++ b/configs/convnext/convnext-tiny_upernet_8xb2-amp-1k_HOTS_v1-640x480.py @@ -0,0 +1,54 @@ +_base_ = [ + '../_base_/models/upernet_convnext.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_1k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth' # noqa +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict( + type='mmpretrain.ConvNeXt', + arch='tiny', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + decode_head=dict( + in_channels=[96, 192, 384, 768], + num_classes=46, + ), + auxiliary_head=dict(in_channels=384, num_classes=46), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)), +) + +optim_wrapper = dict( + _delete_=True, + type='AmpOptimWrapper', + optimizer=dict( + type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05), + paramwise_cfg={ + 'decay_rate': 0.9, + 'decay_type': 'stage_wise', + 'num_layers': 6 + }, + constructor='LearningRateDecayOptimizerConstructor', + loss_scale='dynamic') + +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=150), + dict( + type='PolyLR', + power=1.0, + begin=150, + end=1000, + eta_min=0.0, + by_epoch=False, + ) +] + + diff --git a/configs/convnext/convnext-tiny_upernet_8xb2-amp-1k_HOTS_v1-640x480_pretrained_ade20k.py b/configs/convnext/convnext-tiny_upernet_8xb2-amp-1k_HOTS_v1-640x480_pretrained_ade20k.py new file mode 100644 index 0000000000..5bea66e007 --- /dev/null +++ b/configs/convnext/convnext-tiny_upernet_8xb2-amp-1k_HOTS_v1-640x480_pretrained_ade20k.py @@ -0,0 +1,55 @@ +_base_ = [ + '../_base_/models/upernet_convnext.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_1k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth' # noqa +load_from = "checkpoints/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth" +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict( + type='mmpretrain.ConvNeXt', + arch='tiny', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + decode_head=dict( + in_channels=[96, 192, 384, 768], + num_classes=47, + ), + auxiliary_head=dict(in_channels=384, num_classes=46), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)), +) + +optim_wrapper = dict( + _delete_=True, + type='AmpOptimWrapper', + optimizer=dict( + type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05), + paramwise_cfg={ + 'decay_rate': 0.9, + 'decay_type': 'stage_wise', + 'num_layers': 6 + }, + constructor='LearningRateDecayOptimizerConstructor', + loss_scale='dynamic') + +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=150), + dict( + type='PolyLR', + power=1.0, + begin=150, + end=1000, + eta_min=0.0, + by_epoch=False, + ) +] + + diff --git a/configs/convnext/convnext-tiny_upernet_8xb2-amp-20k_HOTS_v1-640x480.py b/configs/convnext/convnext-tiny_upernet_8xb2-amp-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..d30e08e7c5 --- /dev/null +++ b/configs/convnext/convnext-tiny_upernet_8xb2-amp-20k_HOTS_v1-640x480.py @@ -0,0 +1,54 @@ +_base_ = [ + '../_base_/models/upernet_convnext.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth' # noqa +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict( + type='mmpretrain.ConvNeXt', + arch='tiny', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + decode_head=dict( + in_channels=[96, 192, 384, 768], + num_classes=46, + ), + auxiliary_head=dict(in_channels=384, num_classes=46), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)), +) + +optim_wrapper = dict( + _delete_=True, + type='AmpOptimWrapper', + optimizer=dict( + type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05), + paramwise_cfg={ + 'decay_rate': 0.9, + 'decay_type': 'stage_wise', + 'num_layers': 6 + }, + constructor='LearningRateDecayOptimizerConstructor', + loss_scale='dynamic') + +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + power=1.0, + begin=1500, + end=20000, + eta_min=0.0, + by_epoch=False, + ) +] + + diff --git a/configs/convnext/convnext-tiny_upernet_8xb2-amp-20k_HOTS_v1-640x480_pretrained_ade20k.py b/configs/convnext/convnext-tiny_upernet_8xb2-amp-20k_HOTS_v1-640x480_pretrained_ade20k.py new file mode 100644 index 0000000000..177b509b68 --- /dev/null +++ b/configs/convnext/convnext-tiny_upernet_8xb2-amp-20k_HOTS_v1-640x480_pretrained_ade20k.py @@ -0,0 +1,55 @@ +_base_ = [ + '../_base_/models/upernet_convnext.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth' # noqa +load_from = "checkpoints/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth" +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict( + type='mmpretrain.ConvNeXt', + arch='tiny', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + decode_head=dict( + in_channels=[96, 192, 384, 768], + num_classes=46, + ), + auxiliary_head=dict(in_channels=384, num_classes=46), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)), +) + +optim_wrapper = dict( + _delete_=True, + type='AmpOptimWrapper', + optimizer=dict( + type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05), + paramwise_cfg={ + 'decay_rate': 0.9, + 'decay_type': 'stage_wise', + 'num_layers': 6 + }, + constructor='LearningRateDecayOptimizerConstructor', + loss_scale='dynamic') + +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + power=1.0, + begin=1500, + end=20000, + eta_min=0.0, + by_epoch=False, + ) +] + + diff --git a/configs/convnext/convnext-tiny_upernet_8xb2-amp-40k_HOTS_v1-640x480.py b/configs/convnext/convnext-tiny_upernet_8xb2-amp-40k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..15212e5b90 --- /dev/null +++ b/configs/convnext/convnext-tiny_upernet_8xb2-amp-40k_HOTS_v1-640x480.py @@ -0,0 +1,54 @@ +_base_ = [ + '../_base_/models/upernet_convnext.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth' # noqa +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict( + type='mmpretrain.ConvNeXt', + arch='tiny', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + decode_head=dict( + in_channels=[96, 192, 384, 768], + num_classes=46, + ), + auxiliary_head=dict(in_channels=384, num_classes=46), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)), +) + +optim_wrapper = dict( + _delete_=True, + type='AmpOptimWrapper', + optimizer=dict( + type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05), + paramwise_cfg={ + 'decay_rate': 0.9, + 'decay_type': 'stage_wise', + 'num_layers': 6 + }, + constructor='LearningRateDecayOptimizerConstructor', + loss_scale='dynamic') + +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + power=1.0, + begin=1500, + end=40000, + eta_min=0.0, + by_epoch=False, + ) +] + + diff --git a/configs/convnext/convnext-tiny_upernet_8xb2-amp-40k_HOTS_v1-640x480_pretrained_ade20k.py b/configs/convnext/convnext-tiny_upernet_8xb2-amp-40k_HOTS_v1-640x480_pretrained_ade20k.py new file mode 100644 index 0000000000..c88838d9e4 --- /dev/null +++ b/configs/convnext/convnext-tiny_upernet_8xb2-amp-40k_HOTS_v1-640x480_pretrained_ade20k.py @@ -0,0 +1,55 @@ +_base_ = [ + '../_base_/models/upernet_convnext.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth' # noqa +load_from = "checkpoints/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth" +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict( + type='mmpretrain.ConvNeXt', + arch='tiny', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + decode_head=dict( + in_channels=[96, 192, 384, 768], + num_classes=46, + ), + auxiliary_head=dict(in_channels=384, num_classes=46), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)), +) + +optim_wrapper = dict( + _delete_=True, + type='AmpOptimWrapper', + optimizer=dict( + type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05), + paramwise_cfg={ + 'decay_rate': 0.9, + 'decay_type': 'stage_wise', + 'num_layers': 6 + }, + constructor='LearningRateDecayOptimizerConstructor', + loss_scale='dynamic') + +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + power=1.0, + begin=1500, + end=40000, + eta_min=0.0, + by_epoch=False, + ) +] + + diff --git a/configs/convnext/convnext-tiny_upernet_8xb2-amp-5k_HOTS_v1-640x480.py b/configs/convnext/convnext-tiny_upernet_8xb2-amp-5k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..d8996e5aa7 --- /dev/null +++ b/configs/convnext/convnext-tiny_upernet_8xb2-amp-5k_HOTS_v1-640x480.py @@ -0,0 +1,54 @@ +_base_ = [ + '../_base_/models/upernet_convnext.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_5k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth' # noqa +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict( + type='mmpretrain.ConvNeXt', + arch='tiny', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + decode_head=dict( + in_channels=[96, 192, 384, 768], + num_classes=46, + ), + auxiliary_head=dict(in_channels=384, num_classes=46), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)), +) + +optim_wrapper = dict( + _delete_=True, + type='AmpOptimWrapper', + optimizer=dict( + type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05), + paramwise_cfg={ + 'decay_rate': 0.9, + 'decay_type': 'stage_wise', + 'num_layers': 6 + }, + constructor='LearningRateDecayOptimizerConstructor', + loss_scale='dynamic') + +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=150), + dict( + type='PolyLR', + power=1.0, + begin=150, + end=5000, + eta_min=0.0, + by_epoch=False, + ) +] + + diff --git a/configs/convnext/convnext-tiny_upernet_8xb2-amp-5k_HOTS_v1-640x480_pretrained_ade20k.py b/configs/convnext/convnext-tiny_upernet_8xb2-amp-5k_HOTS_v1-640x480_pretrained_ade20k.py new file mode 100644 index 0000000000..6a0a47aa4b --- /dev/null +++ b/configs/convnext/convnext-tiny_upernet_8xb2-amp-5k_HOTS_v1-640x480_pretrained_ade20k.py @@ -0,0 +1,55 @@ +_base_ = [ + '../_base_/models/upernet_convnext.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_5k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth' # noqa +load_from = "checkpoints/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth" +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict( + type='mmpretrain.ConvNeXt', + arch='tiny', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + decode_head=dict( + in_channels=[96, 192, 384, 768], + num_classes=46, + ), + auxiliary_head=dict(in_channels=384, num_classes=46), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)), +) + +optim_wrapper = dict( + _delete_=True, + type='AmpOptimWrapper', + optimizer=dict( + type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05), + paramwise_cfg={ + 'decay_rate': 0.9, + 'decay_type': 'stage_wise', + 'num_layers': 6 + }, + constructor='LearningRateDecayOptimizerConstructor', + loss_scale='dynamic') + +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=150), + dict( + type='PolyLR', + power=1.0, + begin=150, + end=5000, + eta_min=0.0, + by_epoch=False, + ) +] + + diff --git a/configs/convnext/convnext-tiny_upernet_8xb2-amp-5k_HOTS_v1-640x480_test.py b/configs/convnext/convnext-tiny_upernet_8xb2-amp-5k_HOTS_v1-640x480_test.py new file mode 100644 index 0000000000..d8996e5aa7 --- /dev/null +++ b/configs/convnext/convnext-tiny_upernet_8xb2-amp-5k_HOTS_v1-640x480_test.py @@ -0,0 +1,54 @@ +_base_ = [ + '../_base_/models/upernet_convnext.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_5k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth' # noqa +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict( + type='mmpretrain.ConvNeXt', + arch='tiny', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + decode_head=dict( + in_channels=[96, 192, 384, 768], + num_classes=46, + ), + auxiliary_head=dict(in_channels=384, num_classes=46), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)), +) + +optim_wrapper = dict( + _delete_=True, + type='AmpOptimWrapper', + optimizer=dict( + type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05), + paramwise_cfg={ + 'decay_rate': 0.9, + 'decay_type': 'stage_wise', + 'num_layers': 6 + }, + constructor='LearningRateDecayOptimizerConstructor', + loss_scale='dynamic') + +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=150), + dict( + type='PolyLR', + power=1.0, + begin=150, + end=5000, + eta_min=0.0, + by_epoch=False, + ) +] + + diff --git a/configs/convnext/convnext-tiny_upernet_8xb2-amp-80k_HOTS_v1-640x480.py b/configs/convnext/convnext-tiny_upernet_8xb2-amp-80k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..d9444ee765 --- /dev/null +++ b/configs/convnext/convnext-tiny_upernet_8xb2-amp-80k_HOTS_v1-640x480.py @@ -0,0 +1,54 @@ +_base_ = [ + '../_base_/models/upernet_convnext.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth' # noqa +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict( + type='mmpretrain.ConvNeXt', + arch='tiny', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + decode_head=dict( + in_channels=[96, 192, 384, 768], + num_classes=46, + ), + auxiliary_head=dict(in_channels=384, num_classes=46), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)), +) + +optim_wrapper = dict( + _delete_=True, + type='AmpOptimWrapper', + optimizer=dict( + type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05), + paramwise_cfg={ + 'decay_rate': 0.9, + 'decay_type': 'stage_wise', + 'num_layers': 6 + }, + constructor='LearningRateDecayOptimizerConstructor', + loss_scale='dynamic') + +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + power=1.0, + begin=1500, + end=80000, + eta_min=0.0, + by_epoch=False, + ) +] + + diff --git a/configs/convnext/convnext-tiny_upernet_8xb2-amp-80k_HOTS_v1-640x480_pretrained_ade20k.py b/configs/convnext/convnext-tiny_upernet_8xb2-amp-80k_HOTS_v1-640x480_pretrained_ade20k.py new file mode 100644 index 0000000000..87b28cb896 --- /dev/null +++ b/configs/convnext/convnext-tiny_upernet_8xb2-amp-80k_HOTS_v1-640x480_pretrained_ade20k.py @@ -0,0 +1,55 @@ +_base_ = [ + '../_base_/models/upernet_convnext.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth' # noqa +load_from = "checkpoints/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth" +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict( + type='mmpretrain.ConvNeXt', + arch='tiny', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + decode_head=dict( + in_channels=[96, 192, 384, 768], + num_classes=46, + ), + auxiliary_head=dict(in_channels=384, num_classes=46), + test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)), +) + +optim_wrapper = dict( + _delete_=True, + type='AmpOptimWrapper', + optimizer=dict( + type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05), + paramwise_cfg={ + 'decay_rate': 0.9, + 'decay_type': 'stage_wise', + 'num_layers': 6 + }, + constructor='LearningRateDecayOptimizerConstructor', + loss_scale='dynamic') + +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + power=1.0, + begin=1500, + end=80000, + eta_min=0.0, + by_epoch=False, + ) +] + + diff --git a/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-10k_HOTS_v1-640x480.py b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-10k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..989ece220f --- /dev/null +++ b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-10k_HOTS_v1-640x480.py @@ -0,0 +1,63 @@ +_base_ = [ + '../_base_/datasets/hots_v1_640x480.py', '../_base_/schedules/schedule_10k.py', + '../_base_/default_runtime.py', +] + +# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa +# Licensed under the MIT License +# class_weight = [ +# 0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786, +# 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529, +# 1.0507 +# ] +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/pretrain/ddrnet23s-in1kpre_3rdparty-1ccac5b1.pth' # noqa +crop_size = (640, 480) +data_preprocessor = dict( + type='SegDataPreProcessor', + size=crop_size, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='DDRNet', + in_channels=3, + channels=32, + ppm_channels=128, + norm_cfg=norm_cfg, + align_corners=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), + decode_head=dict( + type='DDRHead', + in_channels=32 * 4, + channels=64, + dropout_ratio=0., + num_classes=46, + align_corners=False, + norm_cfg=norm_cfg, + loss_decode=[ + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=1.0), + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=0.4), + ]), + + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) + + +randomness = dict(seed=304) diff --git a/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-10k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-10k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..833a122b63 --- /dev/null +++ b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-10k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,64 @@ +_base_ = [ + '../_base_/datasets/hots_v1_640x480.py', '../_base_/schedules/schedule_10k.py', + '../_base_/default_runtime.py', +] + +# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa +# Licensed under the MIT License +# class_weight = [ +# 0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786, +# 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529, +# 1.0507 +# ] +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/pretrain/ddrnet23s-in1kpre_3rdparty-1ccac5b1.pth' # noqa +crop_size = (640, 480) +data_preprocessor = dict( + type='SegDataPreProcessor', + size=crop_size, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +norm_cfg = dict(type='SyncBN', requires_grad=True) +load_from = "checkpoints/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024_20230426_145312-6a5e5174.pth" +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='DDRNet', + in_channels=3, + channels=32, + ppm_channels=128, + norm_cfg=norm_cfg, + align_corners=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), + decode_head=dict( + type='DDRHead', + in_channels=32 * 4, + channels=64, + dropout_ratio=0., + num_classes=46, + align_corners=False, + norm_cfg=norm_cfg, + loss_decode=[ + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=1.0), + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=0.4), + ]), + + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) + + +randomness = dict(seed=304) diff --git a/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-160k_HOTS_v1-640x480.py b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-160k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..5d23666edf --- /dev/null +++ b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-160k_HOTS_v1-640x480.py @@ -0,0 +1,63 @@ +_base_ = [ + '../_base_/datasets/hots_v1_640x480.py', '../_base_/schedules/schedule_160k.py', + '../_base_/default_runtime.py', +] + +# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa +# Licensed under the MIT License +# class_weight = [ +# 0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786, +# 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529, +# 1.0507 +# ] +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/pretrain/ddrnet23s-in1kpre_3rdparty-1ccac5b1.pth' # noqa +crop_size = (640, 480) +data_preprocessor = dict( + type='SegDataPreProcessor', + size=crop_size, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='DDRNet', + in_channels=3, + channels=32, + ppm_channels=128, + norm_cfg=norm_cfg, + align_corners=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), + decode_head=dict( + type='DDRHead', + in_channels=32 * 4, + channels=64, + dropout_ratio=0., + num_classes=46, + align_corners=False, + norm_cfg=norm_cfg, + loss_decode=[ + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=1.0), + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=0.4), + ]), + + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) + + +randomness = dict(seed=304) diff --git a/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-160k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-160k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..321e224a55 --- /dev/null +++ b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-160k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,64 @@ +_base_ = [ + '../_base_/datasets/hots_v1_640x480.py', '../_base_/schedules/schedule_160k.py', + '../_base_/default_runtime.py', +] + +# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa +# Licensed under the MIT License +# class_weight = [ +# 0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786, +# 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529, +# 1.0507 +# ] +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/pretrain/ddrnet23s-in1kpre_3rdparty-1ccac5b1.pth' # noqa +crop_size = (640, 480) +data_preprocessor = dict( + type='SegDataPreProcessor', + size=crop_size, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +norm_cfg = dict(type='SyncBN', requires_grad=True) +load_from = "checkpoints/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024_20230426_145312-6a5e5174.pth" +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='DDRNet', + in_channels=3, + channels=32, + ppm_channels=128, + norm_cfg=norm_cfg, + align_corners=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), + decode_head=dict( + type='DDRHead', + in_channels=32 * 4, + channels=64, + dropout_ratio=0., + num_classes=46, + align_corners=False, + norm_cfg=norm_cfg, + loss_decode=[ + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=1.0), + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=0.4), + ]), + + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) + + +randomness = dict(seed=304) diff --git a/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-1k_HOTS_v1-640x480.py b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-1k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..bb5150cda8 --- /dev/null +++ b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-1k_HOTS_v1-640x480.py @@ -0,0 +1,64 @@ +_base_ = [ + '../_base_/datasets/hots_v1_640x480.py', '../_base_/schedules/schedule_1k.py', + '../_base_/default_runtime.py', +] + +# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa +# Licensed under the MIT License +# class_weight = [ +# 0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786, +# 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529, +# 1.0507 +# ] +class_weight = [1.0] * (46 - 1) + [0.1] +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/pretrain/ddrnet23s-in1kpre_3rdparty-1ccac5b1.pth' # noqa +crop_size = (640, 480) +data_preprocessor = dict( + type='SegDataPreProcessor', + size=crop_size, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='DDRNet', + in_channels=3, + channels=32, + ppm_channels=128, + norm_cfg=norm_cfg, + align_corners=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), + decode_head=dict( + type='DDRHead', + in_channels=32 * 4, + channels=64, + dropout_ratio=0., + num_classes=46, + align_corners=False, + norm_cfg=norm_cfg, + loss_decode=[ + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + class_weight=class_weight, + loss_weight=1.0), + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + class_weight=class_weight, + loss_weight=0.4), + ]), + + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) + + +randomness = dict(seed=304) diff --git a/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-1k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-1k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..489540489b --- /dev/null +++ b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-1k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,64 @@ +_base_ = [ + '../_base_/datasets/hots_v1_640x480.py', '../_base_/schedules/schedule_1k.py', + '../_base_/default_runtime.py', +] + +# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa +# Licensed under the MIT License +# class_weight = [ +# 0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786, +# 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529, +# 1.0507 +# ] +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/pretrain/ddrnet23s-in1kpre_3rdparty-1ccac5b1.pth' # noqa +crop_size = (640, 480) +data_preprocessor = dict( + type='SegDataPreProcessor', + size=crop_size, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +norm_cfg = dict(type='SyncBN', requires_grad=True) +load_from = "checkpoints/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024_20230426_145312-6a5e5174.pth" +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='DDRNet', + in_channels=3, + channels=32, + ppm_channels=128, + norm_cfg=norm_cfg, + align_corners=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), + decode_head=dict( + type='DDRHead', + in_channels=32 * 4, + channels=64, + dropout_ratio=0., + num_classes=46, + align_corners=False, + norm_cfg=norm_cfg, + loss_decode=[ + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=1.0), + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=0.4), + ]), + + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) + + +randomness = dict(seed=304) diff --git a/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-20k_HOTS_v1-640x480.py b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..6b1bd92452 --- /dev/null +++ b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-20k_HOTS_v1-640x480.py @@ -0,0 +1,63 @@ +_base_ = [ + '../_base_/datasets/hots_v1_640x480.py', '../_base_/schedules/schedule_20k.py', + '../_base_/default_runtime.py', +] + +# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa +# Licensed under the MIT License +# class_weight = [ +# 0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786, +# 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529, +# 1.0507 +# ] +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/pretrain/ddrnet23s-in1kpre_3rdparty-1ccac5b1.pth' # noqa +crop_size = (640, 480) +data_preprocessor = dict( + type='SegDataPreProcessor', + size=crop_size, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='DDRNet', + in_channels=3, + channels=32, + ppm_channels=128, + norm_cfg=norm_cfg, + align_corners=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), + decode_head=dict( + type='DDRHead', + in_channels=32 * 4, + channels=64, + dropout_ratio=0., + num_classes=46, + align_corners=False, + norm_cfg=norm_cfg, + loss_decode=[ + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=1.0), + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=0.4), + ]), + + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) + + +randomness = dict(seed=304) diff --git a/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-20k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-20k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..dc063e9c73 --- /dev/null +++ b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-20k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,64 @@ +_base_ = [ + '../_base_/datasets/hots_v1_640x480.py', '../_base_/schedules/schedule_20k.py', + '../_base_/default_runtime.py', +] + +# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa +# Licensed under the MIT License +# class_weight = [ +# 0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786, +# 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529, +# 1.0507 +# ] +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/pretrain/ddrnet23s-in1kpre_3rdparty-1ccac5b1.pth' # noqa +crop_size = (640, 480) +data_preprocessor = dict( + type='SegDataPreProcessor', + size=crop_size, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +norm_cfg = dict(type='SyncBN', requires_grad=True) +load_from = "checkpoints/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024_20230426_145312-6a5e5174.pth" +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='DDRNet', + in_channels=3, + channels=32, + ppm_channels=128, + norm_cfg=norm_cfg, + align_corners=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), + decode_head=dict( + type='DDRHead', + in_channels=32 * 4, + channels=64, + dropout_ratio=0., + num_classes=46, + align_corners=False, + norm_cfg=norm_cfg, + loss_decode=[ + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=1.0), + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=0.4), + ]), + + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) + + +randomness = dict(seed=304) diff --git a/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-40k_HOTS_v1-640x480.py b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-40k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..c7b7d2fa27 --- /dev/null +++ b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-40k_HOTS_v1-640x480.py @@ -0,0 +1,63 @@ +_base_ = [ + '../_base_/datasets/hots_v1_640x480.py', '../_base_/schedules/schedule_40k.py', + '../_base_/default_runtime.py', +] + +# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa +# Licensed under the MIT License +# class_weight = [ +# 0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786, +# 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529, +# 1.0507 +# ] +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/pretrain/ddrnet23s-in1kpre_3rdparty-1ccac5b1.pth' # noqa +crop_size = (640, 480) +data_preprocessor = dict( + type='SegDataPreProcessor', + size=crop_size, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='DDRNet', + in_channels=3, + channels=32, + ppm_channels=128, + norm_cfg=norm_cfg, + align_corners=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), + decode_head=dict( + type='DDRHead', + in_channels=32 * 4, + channels=64, + dropout_ratio=0., + num_classes=46, + align_corners=False, + norm_cfg=norm_cfg, + loss_decode=[ + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=1.0), + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=0.4), + ]), + + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) + + +randomness = dict(seed=304) diff --git a/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-40k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-40k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..5fe90af667 --- /dev/null +++ b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-40k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,64 @@ +_base_ = [ + '../_base_/datasets/hots_v1_640x480.py', '../_base_/schedules/schedule_40k.py', + '../_base_/default_runtime.py', +] + +# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa +# Licensed under the MIT License +# class_weight = [ +# 0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786, +# 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529, +# 1.0507 +# ] +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/pretrain/ddrnet23s-in1kpre_3rdparty-1ccac5b1.pth' # noqa +crop_size = (640, 480) +data_preprocessor = dict( + type='SegDataPreProcessor', + size=crop_size, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +norm_cfg = dict(type='SyncBN', requires_grad=True) +load_from = "checkpoints/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024_20230426_145312-6a5e5174.pth" +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='DDRNet', + in_channels=3, + channels=32, + ppm_channels=128, + norm_cfg=norm_cfg, + align_corners=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), + decode_head=dict( + type='DDRHead', + in_channels=32 * 4, + channels=64, + dropout_ratio=0., + num_classes=46, + align_corners=False, + norm_cfg=norm_cfg, + loss_decode=[ + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=1.0), + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=0.4), + ]), + + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) + + +randomness = dict(seed=304) diff --git a/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-5k_HOTS_v1-640x480.py b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-5k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..73d6cb244f --- /dev/null +++ b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-5k_HOTS_v1-640x480.py @@ -0,0 +1,63 @@ +_base_ = [ + '../_base_/datasets/hots_v1_640x480.py', '../_base_/schedules/schedule_5k.py', + '../_base_/default_runtime.py', +] + +# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa +# Licensed under the MIT License +# class_weight = [ +# 0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786, +# 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529, +# 1.0507 +# ] +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/pretrain/ddrnet23s-in1kpre_3rdparty-1ccac5b1.pth' # noqa +crop_size = (640, 480) +data_preprocessor = dict( + type='SegDataPreProcessor', + size=crop_size, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='DDRNet', + in_channels=3, + channels=32, + ppm_channels=128, + norm_cfg=norm_cfg, + align_corners=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), + decode_head=dict( + type='DDRHead', + in_channels=32 * 4, + channels=64, + dropout_ratio=0., + num_classes=46, + align_corners=False, + norm_cfg=norm_cfg, + loss_decode=[ + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=1.0), + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=0.4), + ]), + + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) + + +randomness = dict(seed=304) diff --git a/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-5k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-5k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..62af5c1e39 --- /dev/null +++ b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-5k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,64 @@ +_base_ = [ + '../_base_/datasets/hots_v1_640x480.py', '../_base_/schedules/schedule_5k.py', + '../_base_/default_runtime.py', +] + +# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa +# Licensed under the MIT License +# class_weight = [ +# 0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786, +# 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529, +# 1.0507 +# ] +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/pretrain/ddrnet23s-in1kpre_3rdparty-1ccac5b1.pth' # noqa +crop_size = (640, 480) +data_preprocessor = dict( + type='SegDataPreProcessor', + size=crop_size, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +norm_cfg = dict(type='SyncBN', requires_grad=True) +load_from = "checkpoints/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024_20230426_145312-6a5e5174.pth" +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='DDRNet', + in_channels=3, + channels=32, + ppm_channels=128, + norm_cfg=norm_cfg, + align_corners=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), + decode_head=dict( + type='DDRHead', + in_channels=32 * 4, + channels=64, + dropout_ratio=0., + num_classes=46, + align_corners=False, + norm_cfg=norm_cfg, + loss_decode=[ + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=1.0), + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=0.4), + ]), + + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) + + +randomness = dict(seed=304) diff --git a/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-80k_HOTS_v1-640x480.py b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-80k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..f03bca358f --- /dev/null +++ b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-80k_HOTS_v1-640x480.py @@ -0,0 +1,63 @@ +_base_ = [ + '../_base_/datasets/hots_v1_640x480.py', '../_base_/schedules/schedule_80k.py', + '../_base_/default_runtime.py', +] + +# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa +# Licensed under the MIT License +# class_weight = [ +# 0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786, +# 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529, +# 1.0507 +# ] +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/pretrain/ddrnet23s-in1kpre_3rdparty-1ccac5b1.pth' # noqa +crop_size = (640, 480) +data_preprocessor = dict( + type='SegDataPreProcessor', + size=crop_size, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='DDRNet', + in_channels=3, + channels=32, + ppm_channels=128, + norm_cfg=norm_cfg, + align_corners=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), + decode_head=dict( + type='DDRHead', + in_channels=32 * 4, + channels=64, + dropout_ratio=0., + num_classes=46, + align_corners=False, + norm_cfg=norm_cfg, + loss_decode=[ + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=1.0), + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=0.4), + ]), + + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) + + +randomness = dict(seed=304) diff --git a/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-80k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-80k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..0b19cc96e2 --- /dev/null +++ b/configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-80k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,64 @@ +_base_ = [ + '../_base_/datasets/hots_v1_640x480.py', '../_base_/schedules/schedule_80k.py', + '../_base_/default_runtime.py', +] + +# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa +# Licensed under the MIT License +# class_weight = [ +# 0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786, +# 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529, +# 1.0507 +# ] +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/pretrain/ddrnet23s-in1kpre_3rdparty-1ccac5b1.pth' # noqa +crop_size = (640, 480) +data_preprocessor = dict( + type='SegDataPreProcessor', + size=crop_size, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +norm_cfg = dict(type='SyncBN', requires_grad=True) +load_from = "checkpoints/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024_20230426_145312-6a5e5174.pth" +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='DDRNet', + in_channels=3, + channels=32, + ppm_channels=128, + norm_cfg=norm_cfg, + align_corners=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), + decode_head=dict( + type='DDRHead', + in_channels=32 * 4, + channels=64, + dropout_ratio=0., + num_classes=46, + align_corners=False, + norm_cfg=norm_cfg, + loss_decode=[ + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=1.0), + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + # class_weight=class_weight, + loss_weight=0.4), + ]), + + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) + + +randomness = dict(seed=304) diff --git a/configs/deeplabv3/deeplabv3_r18-d8_4xb2-10k_HOTS_v1-640x480.py b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-10k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..6cd8fc4196 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-10k_HOTS_v1-640x480.py @@ -0,0 +1,19 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_10k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18-d8_4xb2-10k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-10k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..c20a9ee5e5 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-10k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,20 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_10k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth" +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18-d8_4xb2-1k_HOTS_v1-640x480.py b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-1k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..5865bc8642 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-1k_HOTS_v1-640x480.py @@ -0,0 +1,19 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_1k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18-d8_4xb2-1k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-1k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..7d76ab8588 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-1k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,20 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_1k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth" +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18-d8_4xb2-20k_HOTS_v1-640x480.py b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..7acf04cd0d --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,19 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..453a87b22f --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,20 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth" +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18-d8_4xb2-40k_HOTS_v1-640x480.py b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-40k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..d3d4b8b53a --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-40k_HOTS_v1-640x480.py @@ -0,0 +1,19 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18-d8_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..ad3a661d83 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,20 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth" +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18-d8_4xb2-5k_HOTS_v1-640x480.py b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-5k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..d02710cdf0 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-5k_HOTS_v1-640x480.py @@ -0,0 +1,19 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_5k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18-d8_4xb2-5k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-5k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..93f594b654 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-5k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,20 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_5k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth" +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_HOTS_v1-640x480.py b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..08fe3c01be --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_HOTS_v1-640x480.py @@ -0,0 +1,19 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..bcd3a1c3b0 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18-d8_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,20 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3_r18-d8_512x1024_80k_cityscapes_20201225_021506-23dffbe2.pth" +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-10k_HOTS_v1-640x480.py b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-10k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..d078a53ee9 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-10k_HOTS_v1-640x480.py @@ -0,0 +1,19 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_10k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-10k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-10k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..e9e54e5f4b --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-10k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,20 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_10k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth" +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-1k_HOTS_v1-640x480.py b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-1k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..d52070d8c7 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-1k_HOTS_v1-640x480.py @@ -0,0 +1,19 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_1k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-1k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-1k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..c8a7877aca --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-1k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,20 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_1k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth" +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-20k_HOTS_v1-640x480.py b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..39b7b7d7c4 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,19 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..95f79ba6fb --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,20 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth" +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-40k_HOTS_v1-640x480.py b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-40k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..ed0c0de863 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-40k_HOTS_v1-640x480.py @@ -0,0 +1,19 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..dac191a22d --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,20 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth" +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-5k_HOTS_v1-640x480.py b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-5k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..50f0345280 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-5k_HOTS_v1-640x480.py @@ -0,0 +1,19 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_5k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-5k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-5k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..571d0a79c8 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-5k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,20 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_5k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth" +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_HOTS_v1-640x480.py b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..4417c78942 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_HOTS_v1-640x480.py @@ -0,0 +1,19 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..940eeceb95 --- /dev/null +++ b/configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,20 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3_r18b-d8_512x1024_80k_cityscapes_20201225_094144-46040cef.pth" +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-10k_HOTS_v1-640x480.py b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-10k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..eb6e6f6cd0 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-10k_HOTS_v1-640x480.py @@ -0,0 +1,21 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_10k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-10k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-10k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..7d44bd0c87 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-10k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_10k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth" +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-1k_HOTS_v1-640x480.py b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-1k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..fe7278b9e7 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-1k_HOTS_v1-640x480.py @@ -0,0 +1,21 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_1k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-1k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-1k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..08bbe0c3c8 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-1k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_1k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth" +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-20k_HOTS_v1-640x480.py b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..0151641a42 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,21 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..a6d18b2659 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth" +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-40k_HOTS_v1-640x480.py b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-40k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..4c7fd8c1c3 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-40k_HOTS_v1-640x480.py @@ -0,0 +1,21 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..f779b348d6 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth" +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-5k_HOTS_v1-640x480.py b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-5k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..e551c39187 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-5k_HOTS_v1-640x480.py @@ -0,0 +1,21 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_5k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-5k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-5k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..ea9e397333 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-5k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_5k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth" +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_HOTS_v1-640x480.py b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..44c495ec9e --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_HOTS_v1-640x480.py @@ -0,0 +1,21 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) \ No newline at end of file diff --git a/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..0ea0230e52 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18-d8_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3plus_r18-d8_512x1024_80k_cityscapes_20201226_080942-cff257fe.pth" +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) \ No newline at end of file diff --git a/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-10k_HOTS_v1-640x480.py b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-10k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..11ade975ce --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-10k_HOTS_v1-640x480.py @@ -0,0 +1,21 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_10k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-10k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-10k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..f8fa966647 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-10k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_10k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth" +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-1k_HOTS_v1-640x480.py b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-1k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..e305270aa9 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-1k_HOTS_v1-640x480.py @@ -0,0 +1,21 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_1k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-1k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-1k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..fa6674f5e1 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-1k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_1k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth" +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-20k_HOTS_v1-640x480.py b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..c54b30d042 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,21 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..153ef922d4 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth" +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-40k_HOTS_v1-640x480.py b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-40k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..6765322b70 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-40k_HOTS_v1-640x480.py @@ -0,0 +1,21 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..7a8b1aa683 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth" +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-5k_HOTS_v1-640x480.py b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-5k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..e9530c723a --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-5k_HOTS_v1-640x480.py @@ -0,0 +1,21 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_5k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-5k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-5k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..9f257b1dd1 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-5k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_5k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth" +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_HOTS_v1-640x480.py b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..255fdd30c1 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_HOTS_v1-640x480.py @@ -0,0 +1,21 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..fda1ee8755 --- /dev/null +++ b/configs/deeplabv3plus/deeplabv3plus_r18b-d8_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/deeplabv3plus_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/deeplabv3plus_r18b-d8_512x1024_80k_cityscapes_20201226_090828-e451abd9.pth" +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(depth=18), + decode_head=dict( + c1_in_channels=64, + c1_channels=12, + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46), + data_preprocessor=data_preprocessor) diff --git a/configs/fastscnn/fast_scnn_8xb4-10k_HOTS_v1-640x480.py b/configs/fastscnn/fast_scnn_8xb4-10k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..8dd1827011 --- /dev/null +++ b/configs/fastscnn/fast_scnn_8xb4-10k_HOTS_v1-640x480.py @@ -0,0 +1,50 @@ +_base_ = [ + '../_base_/models/fast_scnn.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_10k.py' +] +norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01) +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) + +# Re-config the data sampler. +train_dataloader = dict(batch_size=2, num_workers=2) +val_dataloader = dict(batch_size=1, num_workers=2) +test_dataloader = val_dataloader + +# Re-config the optimizer. +optimizer = dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=4e-5) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + + decode_head=dict(num_classes=46), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=128, + channels=32, + num_convs=1, + num_classes=46, + in_index=-2, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + dict( + type='FCNHead', + in_channels=64, + channels=32, + num_convs=1, + num_classes=46, + in_index=-3, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) \ No newline at end of file diff --git a/configs/fastscnn/fast_scnn_8xb4-10k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fastscnn/fast_scnn_8xb4-10k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..9808129e60 --- /dev/null +++ b/configs/fastscnn/fast_scnn_8xb4-10k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,51 @@ +_base_ = [ + '../_base_/models/fast_scnn.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_10k.py' +] +norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01) +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) + +# Re-config the data sampler. +train_dataloader = dict(batch_size=2, num_workers=2) +val_dataloader = dict(batch_size=1, num_workers=2) +test_dataloader = val_dataloader + +# Re-config the optimizer. +optimizer = dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=4e-5) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer) +load_from = "checkpoints/fast_scnn_lr0.12_8x4_160k_cityscapes_20210630_164853-0cec9937.pth" +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + + decode_head=dict(num_classes=46), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=128, + channels=32, + num_convs=1, + num_classes=46, + in_index=-2, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + dict( + type='FCNHead', + in_channels=64, + channels=32, + num_convs=1, + num_classes=46, + in_index=-3, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) \ No newline at end of file diff --git a/configs/fastscnn/fast_scnn_8xb4-160k_HOTS_v1-640x480.py b/configs/fastscnn/fast_scnn_8xb4-160k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..740a089f27 --- /dev/null +++ b/configs/fastscnn/fast_scnn_8xb4-160k_HOTS_v1-640x480.py @@ -0,0 +1,50 @@ +_base_ = [ + '../_base_/models/fast_scnn.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01) +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) + +# Re-config the data sampler. +train_dataloader = dict(batch_size=2, num_workers=2) +val_dataloader = dict(batch_size=1, num_workers=2) +test_dataloader = val_dataloader + +# Re-config the optimizer. +optimizer = dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=4e-5) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + + decode_head=dict(num_classes=46), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=128, + channels=32, + num_convs=1, + num_classes=46, + in_index=-2, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + dict( + type='FCNHead', + in_channels=64, + channels=32, + num_convs=1, + num_classes=46, + in_index=-3, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) \ No newline at end of file diff --git a/configs/fastscnn/fast_scnn_8xb4-160k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fastscnn/fast_scnn_8xb4-160k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..72d9a1aa50 --- /dev/null +++ b/configs/fastscnn/fast_scnn_8xb4-160k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,51 @@ +_base_ = [ + '../_base_/models/fast_scnn.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01) +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) + +# Re-config the data sampler. +train_dataloader = dict(batch_size=2, num_workers=2) +val_dataloader = dict(batch_size=1, num_workers=2) +test_dataloader = val_dataloader + +# Re-config the optimizer. +optimizer = dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=4e-5) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer) +load_from = "checkpoints/fast_scnn_lr0.12_8x4_160k_cityscapes_20210630_164853-0cec9937.pth" +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + + decode_head=dict(num_classes=46), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=128, + channels=32, + num_convs=1, + num_classes=46, + in_index=-2, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + dict( + type='FCNHead', + in_channels=64, + channels=32, + num_convs=1, + num_classes=46, + in_index=-3, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) \ No newline at end of file diff --git a/configs/fastscnn/fast_scnn_8xb4-1k_HOTS_v1-640x480.py b/configs/fastscnn/fast_scnn_8xb4-1k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..0da1abd93e --- /dev/null +++ b/configs/fastscnn/fast_scnn_8xb4-1k_HOTS_v1-640x480.py @@ -0,0 +1,50 @@ +_base_ = [ + '../_base_/models/fast_scnn.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_1k.py' +] +norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01) +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) + +# Re-config the data sampler. +train_dataloader = dict(batch_size=2, num_workers=2) +val_dataloader = dict(batch_size=1, num_workers=2) +test_dataloader = val_dataloader + +# Re-config the optimizer. +optimizer = dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=4e-5) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + + decode_head=dict(num_classes=46), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=128, + channels=32, + num_convs=1, + num_classes=46, + in_index=-2, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + dict( + type='FCNHead', + in_channels=64, + channels=32, + num_convs=1, + num_classes=46, + in_index=-3, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) \ No newline at end of file diff --git a/configs/fastscnn/fast_scnn_8xb4-1k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fastscnn/fast_scnn_8xb4-1k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..446aa2e1c8 --- /dev/null +++ b/configs/fastscnn/fast_scnn_8xb4-1k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,51 @@ +_base_ = [ + '../_base_/models/fast_scnn.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_1k.py' +] +norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01) +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) + +# Re-config the data sampler. +train_dataloader = dict(batch_size=2, num_workers=2) +val_dataloader = dict(batch_size=1, num_workers=2) +test_dataloader = val_dataloader + +# Re-config the optimizer. +optimizer = dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=4e-5) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer) +load_from = "checkpoints/fast_scnn_lr0.12_8x4_160k_cityscapes_20210630_164853-0cec9937.pth" +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + + decode_head=dict(num_classes=46), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=128, + channels=32, + num_convs=1, + num_classes=46, + in_index=-2, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + dict( + type='FCNHead', + in_channels=64, + channels=32, + num_convs=1, + num_classes=46, + in_index=-3, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) \ No newline at end of file diff --git a/configs/fastscnn/fast_scnn_8xb4-20k_HOTS_v1-640x480.py b/configs/fastscnn/fast_scnn_8xb4-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..7ec5ad3601 --- /dev/null +++ b/configs/fastscnn/fast_scnn_8xb4-20k_HOTS_v1-640x480.py @@ -0,0 +1,50 @@ +_base_ = [ + '../_base_/models/fast_scnn.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01) +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) + +# Re-config the data sampler. +train_dataloader = dict(batch_size=2, num_workers=2) +val_dataloader = dict(batch_size=1, num_workers=2) +test_dataloader = val_dataloader + +# Re-config the optimizer. +optimizer = dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=4e-5) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + + decode_head=dict(num_classes=46), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=128, + channels=32, + num_convs=1, + num_classes=46, + in_index=-2, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + dict( + type='FCNHead', + in_channels=64, + channels=32, + num_convs=1, + num_classes=46, + in_index=-3, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) \ No newline at end of file diff --git a/configs/fastscnn/fast_scnn_8xb4-20k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fastscnn/fast_scnn_8xb4-20k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..f9d88723de --- /dev/null +++ b/configs/fastscnn/fast_scnn_8xb4-20k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,51 @@ +_base_ = [ + '../_base_/models/fast_scnn.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01) +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) + +# Re-config the data sampler. +train_dataloader = dict(batch_size=2, num_workers=2) +val_dataloader = dict(batch_size=1, num_workers=2) +test_dataloader = val_dataloader + +# Re-config the optimizer. +optimizer = dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=4e-5) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer) +load_from = "checkpoints/fast_scnn_lr0.12_8x4_160k_cityscapes_20210630_164853-0cec9937.pth" +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + + decode_head=dict(num_classes=46), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=128, + channels=32, + num_convs=1, + num_classes=46, + in_index=-2, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + dict( + type='FCNHead', + in_channels=64, + channels=32, + num_convs=1, + num_classes=46, + in_index=-3, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) \ No newline at end of file diff --git a/configs/fastscnn/fast_scnn_8xb4-40k_HOTS_v1-640x480.py b/configs/fastscnn/fast_scnn_8xb4-40k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..574ea17448 --- /dev/null +++ b/configs/fastscnn/fast_scnn_8xb4-40k_HOTS_v1-640x480.py @@ -0,0 +1,50 @@ +_base_ = [ + '../_base_/models/fast_scnn.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01) +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) + +# Re-config the data sampler. +train_dataloader = dict(batch_size=2, num_workers=2) +val_dataloader = dict(batch_size=1, num_workers=2) +test_dataloader = val_dataloader + +# Re-config the optimizer. +optimizer = dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=4e-5) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + + decode_head=dict(num_classes=46), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=128, + channels=32, + num_convs=1, + num_classes=46, + in_index=-2, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + dict( + type='FCNHead', + in_channels=64, + channels=32, + num_convs=1, + num_classes=46, + in_index=-3, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) \ No newline at end of file diff --git a/configs/fastscnn/fast_scnn_8xb4-40k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fastscnn/fast_scnn_8xb4-40k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..a333fb9cbc --- /dev/null +++ b/configs/fastscnn/fast_scnn_8xb4-40k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,51 @@ +_base_ = [ + '../_base_/models/fast_scnn.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01) +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) + +# Re-config the data sampler. +train_dataloader = dict(batch_size=2, num_workers=2) +val_dataloader = dict(batch_size=1, num_workers=2) +test_dataloader = val_dataloader + +# Re-config the optimizer. +optimizer = dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=4e-5) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer) +load_from = "checkpoints/fast_scnn_lr0.12_8x4_160k_cityscapes_20210630_164853-0cec9937.pth" +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + + decode_head=dict(num_classes=46), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=128, + channels=32, + num_convs=1, + num_classes=46, + in_index=-2, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + dict( + type='FCNHead', + in_channels=64, + channels=32, + num_convs=1, + num_classes=46, + in_index=-3, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) \ No newline at end of file diff --git a/configs/fastscnn/fast_scnn_8xb4-5k_HOTS_v1-640x480.py b/configs/fastscnn/fast_scnn_8xb4-5k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..10eb6af9a2 --- /dev/null +++ b/configs/fastscnn/fast_scnn_8xb4-5k_HOTS_v1-640x480.py @@ -0,0 +1,50 @@ +_base_ = [ + '../_base_/models/fast_scnn.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_5k.py' +] +norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01) +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) + +# Re-config the data sampler. +train_dataloader = dict(batch_size=2, num_workers=2) +val_dataloader = dict(batch_size=1, num_workers=2) +test_dataloader = val_dataloader + +# Re-config the optimizer. +optimizer = dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=4e-5) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + + decode_head=dict(num_classes=46), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=128, + channels=32, + num_convs=1, + num_classes=46, + in_index=-2, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + dict( + type='FCNHead', + in_channels=64, + channels=32, + num_convs=1, + num_classes=46, + in_index=-3, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) \ No newline at end of file diff --git a/configs/fastscnn/fast_scnn_8xb4-5k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fastscnn/fast_scnn_8xb4-5k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..f16a999dbf --- /dev/null +++ b/configs/fastscnn/fast_scnn_8xb4-5k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,51 @@ +_base_ = [ + '../_base_/models/fast_scnn.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_5k.py' +] +norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01) +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) + +# Re-config the data sampler. +train_dataloader = dict(batch_size=2, num_workers=2) +val_dataloader = dict(batch_size=1, num_workers=2) +test_dataloader = val_dataloader + +# Re-config the optimizer. +optimizer = dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=4e-5) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer) +load_from = "checkpoints/fast_scnn_lr0.12_8x4_160k_cityscapes_20210630_164853-0cec9937.pth" +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + + decode_head=dict(num_classes=46), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=128, + channels=32, + num_convs=1, + num_classes=46, + in_index=-2, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + dict( + type='FCNHead', + in_channels=64, + channels=32, + num_convs=1, + num_classes=46, + in_index=-3, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) \ No newline at end of file diff --git a/configs/fastscnn/fast_scnn_8xb4-80k_HOTS_v1-640x480.py b/configs/fastscnn/fast_scnn_8xb4-80k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..6db0e6f12e --- /dev/null +++ b/configs/fastscnn/fast_scnn_8xb4-80k_HOTS_v1-640x480.py @@ -0,0 +1,50 @@ +_base_ = [ + '../_base_/models/fast_scnn.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01) +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) + +# Re-config the data sampler. +train_dataloader = dict(batch_size=2, num_workers=2) +val_dataloader = dict(batch_size=1, num_workers=2) +test_dataloader = val_dataloader + +# Re-config the optimizer. +optimizer = dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=4e-5) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + + decode_head=dict(num_classes=46), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=128, + channels=32, + num_convs=1, + num_classes=46, + in_index=-2, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + dict( + type='FCNHead', + in_channels=64, + channels=32, + num_convs=1, + num_classes=46, + in_index=-3, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) \ No newline at end of file diff --git a/configs/fastscnn/fast_scnn_8xb4-80k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fastscnn/fast_scnn_8xb4-80k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..dd6e283338 --- /dev/null +++ b/configs/fastscnn/fast_scnn_8xb4-80k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,51 @@ +_base_ = [ + '../_base_/models/fast_scnn.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01) +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) + +# Re-config the data sampler. +train_dataloader = dict(batch_size=2, num_workers=2) +val_dataloader = dict(batch_size=1, num_workers=2) +test_dataloader = val_dataloader + +# Re-config the optimizer. +optimizer = dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=4e-5) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer) +load_from = "checkpoints/fast_scnn_lr0.12_8x4_160k_cityscapes_20210630_164853-0cec9937.pth" +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + + decode_head=dict(num_classes=46), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=128, + channels=32, + num_convs=1, + num_classes=46, + in_index=-2, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + dict( + type='FCNHead', + in_channels=64, + channels=32, + num_convs=1, + num_classes=46, + in_index=-3, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.4)), + ], + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) \ No newline at end of file diff --git a/configs/fcn/fcn-d6_r101-d16_4xb2-160k_HOTS_v1-640x480.py b/configs/fcn/fcn-d6_r101-d16_4xb2-160k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..0551a06d09 --- /dev/null +++ b/configs/fcn/fcn-d6_r101-d16_4xb2-160k_HOTS_v1-640x480.py @@ -0,0 +1,2 @@ +_base_ = './fcn-d6_r50-d16_4xb2-160k_HOTS_v1-640x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn-d6_r101-d16_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn-d6_r101-d16_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..1404a783f8 --- /dev/null +++ b/configs/fcn/fcn-d6_r101-d16_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,3 @@ +_base_ = './fcn-d6_r50-d16_4xb2-160k_HOTS_v1-640x480.py' +load_from = "checkpoints/fcn_d6_r101-d16_512x1024_40k_cityscapes_20210305_130337-9cf2b450.pth" +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn-d6_r101-d16_4xb2-20k_HOTS_v1-640x480.py b/configs/fcn/fcn-d6_r101-d16_4xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..3656c8da0f --- /dev/null +++ b/configs/fcn/fcn-d6_r101-d16_4xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,2 @@ +_base_ = './fcn-d6_r50-d16_4xb2-20k_HOTS_v1-640x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn-d6_r101-d16_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn-d6_r101-d16_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..572bed3dfd --- /dev/null +++ b/configs/fcn/fcn-d6_r101-d16_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,3 @@ +_base_ = './fcn-d6_r50-d16_4xb2-20k_HOTS_v1-640x480.py' +load_from = "checkpoints/fcn_d6_r101-d16_512x1024_40k_cityscapes_20210305_130337-9cf2b450.pth" +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn-d6_r101-d16_4xb2-40k_HOTS_v1-640x480.py b/configs/fcn/fcn-d6_r101-d16_4xb2-40k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..11ae83a6c2 --- /dev/null +++ b/configs/fcn/fcn-d6_r101-d16_4xb2-40k_HOTS_v1-640x480.py @@ -0,0 +1,2 @@ +_base_ = './fcn-d6_r50-d16_4xb2-40k_HOTS_v1-640x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn-d6_r101-d16_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn-d6_r101-d16_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..d3cec343d4 --- /dev/null +++ b/configs/fcn/fcn-d6_r101-d16_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,3 @@ +_base_ = './fcn-d6_r50-d16_4xb2-40k_HOTS_v1-640x480.py' +load_from = "checkpoints/fcn_d6_r101-d16_512x1024_40k_cityscapes_20210305_130337-9cf2b450.pth" +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn-d6_r101-d16_4xb2-80k_HOTS_v1-640x480.py b/configs/fcn/fcn-d6_r101-d16_4xb2-80k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..236dfb13a0 --- /dev/null +++ b/configs/fcn/fcn-d6_r101-d16_4xb2-80k_HOTS_v1-640x480.py @@ -0,0 +1,2 @@ +_base_ = './fcn-d6_r50-d16_4xb2-80k_HOTS_v1-640x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn-d6_r101-d16_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn-d6_r101-d16_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..af4938ba3e --- /dev/null +++ b/configs/fcn/fcn-d6_r101-d16_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,3 @@ +_base_ = './fcn-d6_r50-d16_4xb2-80k_HOTS_v1-640x480.py' +load_from = "checkpoints/fcn_d6_r101-d16_512x1024_40k_cityscapes_20210305_130337-9cf2b450.pth" +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/fcn/fcn-d6_r101b-d16_4xb2-160k_HOTS_v1-640x480.py b/configs/fcn/fcn-d6_r101b-d16_4xb2-160k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..3cf25311a7 --- /dev/null +++ b/configs/fcn/fcn-d6_r101b-d16_4xb2-160k_HOTS_v1-640x480.py @@ -0,0 +1,4 @@ +_base_ = './fcn-d6_r50-d16_4xb2-160k_HOTS_v1-640x480.py' +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(type='ResNet', depth=101)) diff --git a/configs/fcn/fcn-d6_r101b-d16_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn-d6_r101b-d16_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..2f8ea8dce4 --- /dev/null +++ b/configs/fcn/fcn-d6_r101b-d16_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,5 @@ +_base_ = './fcn-d6_r50-d16_4xb2-160k_HOTS_v1-640x480.py' +load_from = "checkpoints/fcn_d6_r101b-d16_512x1024_80k_cityscapes_20210311_144305-3f2eb5b4.pth" +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(type='ResNet', depth=101)) diff --git a/configs/fcn/fcn-d6_r101b-d16_4xb2-20k_HOTS_v1-640x480.py b/configs/fcn/fcn-d6_r101b-d16_4xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..f87879ec95 --- /dev/null +++ b/configs/fcn/fcn-d6_r101b-d16_4xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,4 @@ +_base_ = './fcn-d6_r50-d16_4xb2-20k_HOTS_v1-640x480.py' +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(type='ResNet', depth=101)) diff --git a/configs/fcn/fcn-d6_r101b-d16_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn-d6_r101b-d16_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..1041fbef44 --- /dev/null +++ b/configs/fcn/fcn-d6_r101b-d16_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,5 @@ +_base_ = './fcn-d6_r50-d16_4xb2-20k_HOTS_v1-640x480.py' +load_from = "checkpoints/fcn_d6_r101b-d16_512x1024_80k_cityscapes_20210311_144305-3f2eb5b4.pth" +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(type='ResNet', depth=101)) diff --git a/configs/fcn/fcn-d6_r101b-d16_4xb2-40k_HOTS_v1-640x480.py b/configs/fcn/fcn-d6_r101b-d16_4xb2-40k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..feaaa637df --- /dev/null +++ b/configs/fcn/fcn-d6_r101b-d16_4xb2-40k_HOTS_v1-640x480.py @@ -0,0 +1,4 @@ +_base_ = './fcn-d6_r50-d16_4xb2-40k_HOTS_v1-640x480.py' +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(type='ResNet', depth=101)) diff --git a/configs/fcn/fcn-d6_r101b-d16_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn-d6_r101b-d16_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..1fc0e66a06 --- /dev/null +++ b/configs/fcn/fcn-d6_r101b-d16_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,5 @@ +_base_ = './fcn-d6_r50-d16_4xb2-40k_HOTS_v1-640x480.py' +load_from = "checkpoints/fcn_d6_r101b-d16_512x1024_80k_cityscapes_20210311_144305-3f2eb5b4.pth" +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(type='ResNet', depth=101)) diff --git a/configs/fcn/fcn-d6_r101b-d16_4xb2-80k_HOTS_v1-640x480.py b/configs/fcn/fcn-d6_r101b-d16_4xb2-80k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..326289f695 --- /dev/null +++ b/configs/fcn/fcn-d6_r101b-d16_4xb2-80k_HOTS_v1-640x480.py @@ -0,0 +1,4 @@ +_base_ = './fcn-d6_r50-d16_4xb2-80k_HOTS_v1-640x480.py' +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(type='ResNet', depth=101)) diff --git a/configs/fcn/fcn-d6_r101b-d16_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn-d6_r101b-d16_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..4e36be8338 --- /dev/null +++ b/configs/fcn/fcn-d6_r101b-d16_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,5 @@ +_base_ = './fcn-d6_r50-d16_4xb2-80k_HOTS_v1-640x480.py' +load_from = "checkpoints/fcn_d6_r101b-d16_512x1024_80k_cityscapes_20210311_144305-3f2eb5b4.pth" +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(type='ResNet', depth=101)) diff --git a/configs/fcn/fcn-d6_r50-d16_4xb2-160k_HOTS_v1-640x480.py b/configs/fcn/fcn-d6_r50-d16_4xb2-160k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..1e05fba121 --- /dev/null +++ b/configs/fcn/fcn-d6_r50-d16_4xb2-160k_HOTS_v1-640x480.py @@ -0,0 +1,18 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict(dilations=(1, 1, 1, 2), strides=(1, 2, 2, 1)), + decode_head=dict( + dilation=6, + num_classes=46 + ), + auxiliary_head=dict( + dilation=6, + num_classes=46 + ) + ) diff --git a/configs/fcn/fcn-d6_r50-d16_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn-d6_r50-d16_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..109fd9e5ec --- /dev/null +++ b/configs/fcn/fcn-d6_r50-d16_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,19 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/fcn_d6_r50-d16_512x1024_40k_cityscapes_20210305_130133-98d5d1bc.pth" +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict(dilations=(1, 1, 1, 2), strides=(1, 2, 2, 1)), + decode_head=dict( + dilation=6, + num_classes=46 + ), + auxiliary_head=dict( + dilation=6, + num_classes=46 + ) + ) diff --git a/configs/fcn/fcn-d6_r50-d16_4xb2-20k_HOTS_v1-640x480.py b/configs/fcn/fcn-d6_r50-d16_4xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..a6849776cb --- /dev/null +++ b/configs/fcn/fcn-d6_r50-d16_4xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,18 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict(dilations=(1, 1, 1, 2), strides=(1, 2, 2, 1)), + decode_head=dict( + dilation=6, + num_classes=46 + ), + auxiliary_head=dict( + dilation=6, + num_classes=46 + ) + ) diff --git a/configs/fcn/fcn-d6_r50-d16_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn-d6_r50-d16_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..11b5aa2640 --- /dev/null +++ b/configs/fcn/fcn-d6_r50-d16_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,19 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/fcn_d6_r50-d16_512x1024_40k_cityscapes_20210305_130133-98d5d1bc.pth" +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict(dilations=(1, 1, 1, 2), strides=(1, 2, 2, 1)), + decode_head=dict( + dilation=6, + num_classes=46 + ), + auxiliary_head=dict( + dilation=6, + num_classes=46 + ) + ) diff --git a/configs/fcn/fcn-d6_r50-d16_4xb2-40k_HOTS_v1-640x480.py b/configs/fcn/fcn-d6_r50-d16_4xb2-40k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..59f5d18b19 --- /dev/null +++ b/configs/fcn/fcn-d6_r50-d16_4xb2-40k_HOTS_v1-640x480.py @@ -0,0 +1,18 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict(dilations=(1, 1, 1, 2), strides=(1, 2, 2, 1)), + decode_head=dict( + dilation=6, + num_classes=46 + ), + auxiliary_head=dict( + dilation=6, + num_classes=46 + ) + ) diff --git a/configs/fcn/fcn-d6_r50-d16_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn-d6_r50-d16_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..ec4fea1b6c --- /dev/null +++ b/configs/fcn/fcn-d6_r50-d16_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,19 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/fcn_d6_r50-d16_512x1024_40k_cityscapes_20210305_130133-98d5d1bc.pth" +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict(dilations=(1, 1, 1, 2), strides=(1, 2, 2, 1)), + decode_head=dict( + dilation=6, + num_classes=46 + ), + auxiliary_head=dict( + dilation=6, + num_classes=46 + ) + ) diff --git a/configs/fcn/fcn-d6_r50-d16_4xb2-80k_HOTS_v1-640x480.py b/configs/fcn/fcn-d6_r50-d16_4xb2-80k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..80b9eece0f --- /dev/null +++ b/configs/fcn/fcn-d6_r50-d16_4xb2-80k_HOTS_v1-640x480.py @@ -0,0 +1,18 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict(dilations=(1, 1, 1, 2), strides=(1, 2, 2, 1)), + decode_head=dict( + dilation=6, + num_classes=46 + ), + auxiliary_head=dict( + dilation=6, + num_classes=46 + ) + ) diff --git a/configs/fcn/fcn-d6_r50-d16_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn-d6_r50-d16_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..29b2026fa8 --- /dev/null +++ b/configs/fcn/fcn-d6_r50-d16_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,19 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/fcn_d6_r50-d16_512x1024_40k_cityscapes_20210305_130133-98d5d1bc.pth" +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict(dilations=(1, 1, 1, 2), strides=(1, 2, 2, 1)), + decode_head=dict( + dilation=6, + num_classes=46 + ), + auxiliary_head=dict( + dilation=6, + num_classes=46 + ) + ) diff --git a/configs/fcn/fcn-d6_r50b-d16_4xb2-160k_HOTS_v1-640x480.py b/configs/fcn/fcn-d6_r50b-d16_4xb2-160k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..8b171c58a7 --- /dev/null +++ b/configs/fcn/fcn-d6_r50b-d16_4xb2-160k_HOTS_v1-640x480.py @@ -0,0 +1,2 @@ +_base_ = './fcn-d6_r50-d16_4xb2-160k_HOTS_v1-640x480.py' +model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) diff --git a/configs/fcn/fcn-d6_r50b-d16_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn-d6_r50b-d16_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..6bcc339442 --- /dev/null +++ b/configs/fcn/fcn-d6_r50b-d16_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,3 @@ +_base_ = './fcn-d6_r50-d16_4xb2-160k_HOTS_v1-640x480.py' +load_from = "checkpoints/fcn_d6_r50b-d16_512x1024_80k_cityscapes_20210311_125550-6a0b62e9.pth" +model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) diff --git a/configs/fcn/fcn-d6_r50b-d16_4xb2-20k_HOTS_v1-640x480.py b/configs/fcn/fcn-d6_r50b-d16_4xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..66a563d16c --- /dev/null +++ b/configs/fcn/fcn-d6_r50b-d16_4xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,2 @@ +_base_ = './fcn-d6_r50-d16_4xb2-20k_HOTS_v1-640x480.py' +model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) diff --git a/configs/fcn/fcn-d6_r50b-d16_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn-d6_r50b-d16_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..6edbdae299 --- /dev/null +++ b/configs/fcn/fcn-d6_r50b-d16_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,3 @@ +_base_ = './fcn-d6_r50-d16_4xb2-20k_HOTS_v1-640x480.py' +load_from = "checkpoints/fcn_d6_r50b-d16_512x1024_80k_cityscapes_20210311_125550-6a0b62e9.pth" +model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) diff --git a/configs/fcn/fcn-d6_r50b-d16_4xb2-40k_HOTS_v1-640x480.py b/configs/fcn/fcn-d6_r50b-d16_4xb2-40k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..b7fe15b282 --- /dev/null +++ b/configs/fcn/fcn-d6_r50b-d16_4xb2-40k_HOTS_v1-640x480.py @@ -0,0 +1,2 @@ +_base_ = './fcn-d6_r50-d16_4xb2-40k_HOTS_v1-640x480.py' +model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) diff --git a/configs/fcn/fcn-d6_r50b-d16_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn-d6_r50b-d16_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..8daeb6c6b9 --- /dev/null +++ b/configs/fcn/fcn-d6_r50b-d16_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,3 @@ +_base_ = './fcn-d6_r50-d16_4xb2-40k_HOTS_v1-640x480.py' +load_from = "checkpoints/fcn_d6_r50b-d16_512x1024_80k_cityscapes_20210311_125550-6a0b62e9.pth" +model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) diff --git a/configs/fcn/fcn-d6_r50b-d16_4xb2-80k_HOTS_v1-640x480.py b/configs/fcn/fcn-d6_r50b-d16_4xb2-80k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..ba4757cc20 --- /dev/null +++ b/configs/fcn/fcn-d6_r50b-d16_4xb2-80k_HOTS_v1-640x480.py @@ -0,0 +1,2 @@ +_base_ = './fcn-d6_r50-d16_4xb2-80k_HOTS_v1-640x480.py' +model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) diff --git a/configs/fcn/fcn-d6_r50b-d16_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn-d6_r50b-d16_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..9b5cc64a60 --- /dev/null +++ b/configs/fcn/fcn-d6_r50b-d16_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,3 @@ +_base_ = './fcn-d6_r50-d16_4xb2-80k_HOTS_v1-640x480.py' +load_from = "checkpoints/fcn_d6_r50b-d16_512x1024_80k_cityscapes_20210311_125550-6a0b62e9.pth" +model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet')) diff --git a/configs/fcn/fcn_r18-d8_4xb2-160k_HOTS_v1-640x480.py b/configs/fcn/fcn_r18-d8_4xb2-160k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..4a62adf33e --- /dev/null +++ b/configs/fcn/fcn_r18-d8_4xb2-160k_HOTS_v1-640x480.py @@ -0,0 +1,21 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46 + ), + data_preprocessor=data_preprocessor) + diff --git a/configs/fcn/fcn_r18-d8_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn_r18-d8_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..67eec93a87 --- /dev/null +++ b/configs/fcn/fcn_r18-d8_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth" +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46 + ), + data_preprocessor=data_preprocessor) + diff --git a/configs/fcn/fcn_r18-d8_4xb2-20k_HOTS_v1-640x480.py b/configs/fcn/fcn_r18-d8_4xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..d94eebcc63 --- /dev/null +++ b/configs/fcn/fcn_r18-d8_4xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,21 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46 + ), + data_preprocessor=data_preprocessor) + diff --git a/configs/fcn/fcn_r18-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn_r18-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..35f81beb4d --- /dev/null +++ b/configs/fcn/fcn_r18-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth" +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46 + ), + data_preprocessor=data_preprocessor) + diff --git a/configs/fcn/fcn_r18-d8_4xb2-40k_HOTS_v1-640x480.py b/configs/fcn/fcn_r18-d8_4xb2-40k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..057c4c198c --- /dev/null +++ b/configs/fcn/fcn_r18-d8_4xb2-40k_HOTS_v1-640x480.py @@ -0,0 +1,21 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46 + ), + data_preprocessor=data_preprocessor) + diff --git a/configs/fcn/fcn_r18-d8_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn_r18-d8_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..20ab45d365 --- /dev/null +++ b/configs/fcn/fcn_r18-d8_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth" +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46 + ), + data_preprocessor=data_preprocessor) + diff --git a/configs/fcn/fcn_r18-d8_4xb2-80k_HOTS_v1-640x480.py b/configs/fcn/fcn_r18-d8_4xb2-80k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..bb931e120c --- /dev/null +++ b/configs/fcn/fcn_r18-d8_4xb2-80k_HOTS_v1-640x480.py @@ -0,0 +1,21 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46 + ), + data_preprocessor=data_preprocessor) + diff --git a/configs/fcn/fcn_r18-d8_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn_r18-d8_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..d777aa4663 --- /dev/null +++ b/configs/fcn/fcn_r18-d8_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/fcn_r18-d8_512x1024_80k_cityscapes_20201225_021327-6c50f8b4.pth" +model = dict( + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46 + ), + data_preprocessor=data_preprocessor) + diff --git a/configs/fcn/fcn_r18b-d8_4xb2-160k_HOTS_v1-640x480.py b/configs/fcn/fcn_r18b-d8_4xb2-160k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..0d1e961212 --- /dev/null +++ b/configs/fcn/fcn_r18b-d8_4xb2-160k_HOTS_v1-640x480.py @@ -0,0 +1,21 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='torchvision://resnet50', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46 + ), + data_preprocessor=data_preprocessor) + diff --git a/configs/fcn/fcn_r18b-d8_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn_r18b-d8_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..e7f9018b1b --- /dev/null +++ b/configs/fcn/fcn_r18b-d8_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth" +model = dict( + pretrained='torchvision://resnet50', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46 + ), + data_preprocessor=data_preprocessor) + diff --git a/configs/fcn/fcn_r18b-d8_4xb2-20k_HOTS_v1-640x480.py b/configs/fcn/fcn_r18b-d8_4xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..a5b2464544 --- /dev/null +++ b/configs/fcn/fcn_r18b-d8_4xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,21 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='torchvision://resnet50', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46 + ), + data_preprocessor=data_preprocessor) + diff --git a/configs/fcn/fcn_r18b-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn_r18b-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..bd43f63330 --- /dev/null +++ b/configs/fcn/fcn_r18b-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth" +model = dict( + pretrained='torchvision://resnet50', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46 + ), + data_preprocessor=data_preprocessor) + diff --git a/configs/fcn/fcn_r18b-d8_4xb2-40k_HOTS_v1-640x480.py b/configs/fcn/fcn_r18b-d8_4xb2-40k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..66b07b1488 --- /dev/null +++ b/configs/fcn/fcn_r18b-d8_4xb2-40k_HOTS_v1-640x480.py @@ -0,0 +1,21 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='torchvision://resnet50', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46 + ), + data_preprocessor=data_preprocessor) + diff --git a/configs/fcn/fcn_r18b-d8_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn_r18b-d8_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..86878cf65b --- /dev/null +++ b/configs/fcn/fcn_r18b-d8_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth" +model = dict( + pretrained='torchvision://resnet50', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46 + ), + data_preprocessor=data_preprocessor) + diff --git a/configs/fcn/fcn_r18b-d8_4xb2-80k_HOTS_v1-640x480.py b/configs/fcn/fcn_r18b-d8_4xb2-80k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..14211d639f --- /dev/null +++ b/configs/fcn/fcn_r18b-d8_4xb2-80k_HOTS_v1-640x480.py @@ -0,0 +1,21 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + pretrained='torchvision://resnet50', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46 + ), + data_preprocessor=data_preprocessor) + diff --git a/configs/fcn/fcn_r18b-d8_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/fcn/fcn_r18b-d8_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..d654be1eb5 --- /dev/null +++ b/configs/fcn/fcn_r18b-d8_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/fcn_r18b-d8_512x1024_80k_cityscapes_20201225_230143-92c0f445.pth" +model = dict( + pretrained='torchvision://resnet50', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=46 + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=46 + ), + data_preprocessor=data_preprocessor) + diff --git a/configs/hrnet/fcn_hr18_4xb2-160k_HOTS_v1-640x480.py b/configs/hrnet/fcn_hr18_4xb2-160k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..2072fc5e28 --- /dev/null +++ b/configs/hrnet/fcn_hr18_4xb2-160k_HOTS_v1-640x480.py @@ -0,0 +1,10 @@ +_base_ = [ + '../_base_/models/fcn_hr18.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=46) + ) diff --git a/configs/hrnet/fcn_hr18_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/hrnet/fcn_hr18_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..9bd00a16e9 --- /dev/null +++ b/configs/hrnet/fcn_hr18_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,11 @@ +_base_ = [ + '../_base_/models/fcn_hr18.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth" +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=46) + ) diff --git a/configs/hrnet/fcn_hr18_4xb2-20k_HOTS_v1-640x480.py b/configs/hrnet/fcn_hr18_4xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..c17541ef6d --- /dev/null +++ b/configs/hrnet/fcn_hr18_4xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,10 @@ +_base_ = [ + '../_base_/models/fcn_hr18.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=46) + ) diff --git a/configs/hrnet/fcn_hr18_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/hrnet/fcn_hr18_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..9876325047 --- /dev/null +++ b/configs/hrnet/fcn_hr18_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,11 @@ +_base_ = [ + '../_base_/models/fcn_hr18.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth" +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=46) + ) diff --git a/configs/hrnet/fcn_hr18_4xb2-40k_HOTS_v1-640x480.py b/configs/hrnet/fcn_hr18_4xb2-40k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..16035d4f15 --- /dev/null +++ b/configs/hrnet/fcn_hr18_4xb2-40k_HOTS_v1-640x480.py @@ -0,0 +1,10 @@ +_base_ = [ + '../_base_/models/fcn_hr18.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=46) + ) diff --git a/configs/hrnet/fcn_hr18_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/hrnet/fcn_hr18_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..014dd40650 --- /dev/null +++ b/configs/hrnet/fcn_hr18_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,11 @@ +_base_ = [ + '../_base_/models/fcn_hr18.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth" +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=46) + ) diff --git a/configs/hrnet/fcn_hr18_4xb2-80k_HOTS_v1-640x480.py b/configs/hrnet/fcn_hr18_4xb2-80k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..ab7b348ea7 --- /dev/null +++ b/configs/hrnet/fcn_hr18_4xb2-80k_HOTS_v1-640x480.py @@ -0,0 +1,10 @@ +_base_ = [ + '../_base_/models/fcn_hr18.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=46) + ) diff --git a/configs/hrnet/fcn_hr18_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/hrnet/fcn_hr18_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..b2df0cf83d --- /dev/null +++ b/configs/hrnet/fcn_hr18_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,11 @@ +_base_ = [ + '../_base_/models/fcn_hr18.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/fcn_hr18_512x1024_80k_cityscapes_20200601_223255-4e7b345e.pth" +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=46) + ) diff --git a/configs/hrnet/fcn_hr18s_4xb2-160k_HOTS_v1-640x480.py b/configs/hrnet/fcn_hr18s_4xb2-160k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..e5c51508d9 --- /dev/null +++ b/configs/hrnet/fcn_hr18s_4xb2-160k_HOTS_v1-640x480.py @@ -0,0 +1,9 @@ +_base_ = './fcn_hr18_4xb2-160k_HOTS_v1-640x480.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/hrnet/fcn_hr18s_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/hrnet/fcn_hr18s_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..4a05baf9a8 --- /dev/null +++ b/configs/hrnet/fcn_hr18s_4xb2-160k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,10 @@ +_base_ = './fcn_hr18_4xb2-160k_HOTS_v1-640x480.py' +load_from = "checkpoints/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth" +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/hrnet/fcn_hr18s_4xb2-20k_HOTS_v1-640x480.py b/configs/hrnet/fcn_hr18s_4xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..85108137a8 --- /dev/null +++ b/configs/hrnet/fcn_hr18s_4xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,9 @@ +_base_ = './fcn_hr18_4xb2-20k_HOTS_v1-640x480.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/hrnet/fcn_hr18s_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/hrnet/fcn_hr18s_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..23e52ac659 --- /dev/null +++ b/configs/hrnet/fcn_hr18s_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,10 @@ +_base_ = './fcn_hr18_4xb2-20k_HOTS_v1-640x480.py' +load_from = "checkpoints/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth" +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/hrnet/fcn_hr18s_4xb2-40k_HOTS_v1-640x480.py b/configs/hrnet/fcn_hr18s_4xb2-40k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..6787f7a05a --- /dev/null +++ b/configs/hrnet/fcn_hr18s_4xb2-40k_HOTS_v1-640x480.py @@ -0,0 +1,9 @@ +_base_ = './fcn_hr18_4xb2-40k_HOTS_v1-640x480.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/hrnet/fcn_hr18s_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/hrnet/fcn_hr18s_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..6b0a217e40 --- /dev/null +++ b/configs/hrnet/fcn_hr18s_4xb2-40k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,10 @@ +_base_ = './fcn_hr18_4xb2-40k_HOTS_v1-640x480.py' +load_from = "checkpoints/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth" +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/hrnet/fcn_hr18s_4xb2-80k_HOTS_v1-640x480.py b/configs/hrnet/fcn_hr18s_4xb2-80k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..6886ce0371 --- /dev/null +++ b/configs/hrnet/fcn_hr18s_4xb2-80k_HOTS_v1-640x480.py @@ -0,0 +1,10 @@ +_base_ = './fcn_hr18_4xb2-80k_HOTS_v1-640x480.py' + +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/hrnet/fcn_hr18s_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/hrnet/fcn_hr18s_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..e33fe6977c --- /dev/null +++ b/configs/hrnet/fcn_hr18s_4xb2-80k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,10 @@ +_base_ = './fcn_hr18_4xb2-80k_HOTS_v1-640x480.py' +load_from = "checkpoints/fcn_hr18s_512x1024_80k_cityscapes_20200601_202700-1462b75d.pth" +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/icnet/icnet_r101-d8-in1k-pre_4xb2-20k_HOTS_v1-640x480.py b/configs/icnet/icnet_r101-d8-in1k-pre_4xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..ef0ec63e1b --- /dev/null +++ b/configs/icnet/icnet_r101-d8-in1k-pre_4xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,7 @@ +_base_ = './icnet_r50-d8_4xb2-20k_HOTS_v1-640x480.py' +model = dict( + backbone=dict( + backbone_cfg=dict( + depth=101, + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnet101_v1c')))) diff --git a/configs/icnet/icnet_r101-d8-in1k-pre_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/icnet/icnet_r101-d8-in1k-pre_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..e106ef5675 --- /dev/null +++ b/configs/icnet/icnet_r101-d8-in1k-pre_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,8 @@ +_base_ = './icnet_r50-d8_4xb2-20k_HOTS_v1-640x480.py' +load_from = "checkpoints/icnet_r101-d8_in1k-pre_832x832_160k_cityscapes_20210925_232612-9484ae8a.pth" +model = dict( + backbone=dict( + backbone_cfg=dict( + depth=101, + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnet101_v1c')))) diff --git a/configs/icnet/icnet_r101-d8_4xb2-20k_HOTS_v1-640x480.py b/configs/icnet/icnet_r101-d8_4xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..833bc13685 --- /dev/null +++ b/configs/icnet/icnet_r101-d8_4xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,2 @@ +_base_ = './icnet_r50-d8_4xb2-20k_HOTS_v1-640x480.py' +model = dict(backbone=dict(backbone_cfg=dict(depth=101))) diff --git a/configs/icnet/icnet_r101-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/icnet/icnet_r101-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..4465af358f --- /dev/null +++ b/configs/icnet/icnet_r101-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,3 @@ +_base_ = './icnet_r50-d8_4xb2-20k_HOTS_v1-640x480.py' +load_from = "checkpoints/icnet_r101-d8_832x832_160k_cityscapes_20210926_092350-3a1ebf1a.pth" +model = dict(backbone=dict(backbone_cfg=dict(depth=101))) diff --git a/configs/icnet/icnet_r18-d8-in1k-pre_4xb2-20k_HOTS_v1-640x480.py b/configs/icnet/icnet_r18-d8-in1k-pre_4xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..55814c6cb0 --- /dev/null +++ b/configs/icnet/icnet_r18-d8-in1k-pre_4xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,8 @@ +_base_ = './icnet_r50-d8_4xb2-20k_HOTS_v1-640x480.py' +model = dict( + backbone=dict( + layer_channels=(128, 512), + backbone_cfg=dict( + depth=18, + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnet18_v1c')))) diff --git a/configs/icnet/icnet_r18-d8-in1k-pre_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/icnet/icnet_r18-d8-in1k-pre_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..31e547f98f --- /dev/null +++ b/configs/icnet/icnet_r18-d8-in1k-pre_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,9 @@ +_base_ = './icnet_r50-d8_4xb2-20k_HOTS_v1-640x480.py' +load_from = "checkpoints/icnet_r18-d8_in1k-pre_832x832_160k_cityscapes_20210926_052702-619c8ae1.pth" +model = dict( + backbone=dict( + layer_channels=(128, 512), + backbone_cfg=dict( + depth=18, + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnet18_v1c')))) diff --git a/configs/icnet/icnet_r18-d8_4xb2-20k_HOTS_v1-640x480.py b/configs/icnet/icnet_r18-d8_4xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..e5c2f98b7b --- /dev/null +++ b/configs/icnet/icnet_r18-d8_4xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,3 @@ +_base_ = './icnet_r50-d8_4xb2-20k_HOTS_v1-640x480.py' +model = dict( + backbone=dict(layer_channels=(128, 512), backbone_cfg=dict(depth=18))) diff --git a/configs/icnet/icnet_r18-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/icnet/icnet_r18-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..5f7ff46f98 --- /dev/null +++ b/configs/icnet/icnet_r18-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,4 @@ +_base_ = './icnet_r50-d8_4xb2-20k_HOTS_v1-640x480.py' +load_from = "checkpoints/icnet_r18-d8_832x832_160k_cityscapes_20210925_230153-2c6eb6e0.pth" +model = dict( + backbone=dict(layer_channels=(128, 512), backbone_cfg=dict(depth=18))) diff --git a/configs/icnet/icnet_r50-d8-in1k-pre_4xb2-20k_HOTS_v1-640x480.py b/configs/icnet/icnet_r50-d8-in1k-pre_4xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..3b5a9deafd --- /dev/null +++ b/configs/icnet/icnet_r50-d8-in1k-pre_4xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,6 @@ +_base_ = './icnet_r50-d8_4xb2-20k_HOTS_v1-640x480.py' +model = dict( + backbone=dict( + backbone_cfg=dict( + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnet50_v1c')))) diff --git a/configs/icnet/icnet_r50-d8-in1k-pre_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/icnet/icnet_r50-d8-in1k-pre_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..a64ffd7e96 --- /dev/null +++ b/configs/icnet/icnet_r50-d8-in1k-pre_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,7 @@ +_base_ = './icnet_r50-d8_4xb2-20k_HOTS_v1-640x480.py' +load_from = "checkpoints/icnet_r50-d8_in1k-pre_832x832_160k_cityscapes_20210926_042715-ce310aea.pth" +model = dict( + backbone=dict( + backbone_cfg=dict( + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnet50_v1c')))) diff --git a/configs/icnet/icnet_r50-d8_4xb2-20k_HOTS_v1-640x480.py b/configs/icnet/icnet_r50-d8_4xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..03fc821e6b --- /dev/null +++ b/configs/icnet/icnet_r50-d8_4xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,41 @@ +_base_ = [ + '../_base_/models/icnet_r50-d8.py', + '../_base_/datasets/hots_v1_640x480.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +norm_cfg = dict(type='SyncBN', requires_grad=True) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict( + num_classes=46 + ), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=128, + channels=128, + num_convs=1, + num_classes=46, + in_index=0, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + dict( + type='FCNHead', + in_channels=128, + channels=128, + num_convs=1, + num_classes=46, + in_index=1, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + ] + + ) diff --git a/configs/icnet/icnet_r50-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py b/configs/icnet/icnet_r50-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py new file mode 100644 index 0000000000..00a13fc865 --- /dev/null +++ b/configs/icnet/icnet_r50-d8_4xb2-20k_HOTS_v1-640x480_pretrained_cityscapes.py @@ -0,0 +1,42 @@ +_base_ = [ + '../_base_/models/icnet_r50-d8.py', + '../_base_/datasets/hots_v1_640x480.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +norm_cfg = dict(type='SyncBN', requires_grad=True) +data_preprocessor = dict(size=crop_size) +load_from = "checkpoints/icnet_r50-d8_832x832_160k_cityscapes_20210925_232612-a95f0d4e.pth" +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict( + num_classes=46 + ), + auxiliary_head=[ + dict( + type='FCNHead', + in_channels=128, + channels=128, + num_convs=1, + num_classes=46, + in_index=0, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + dict( + type='FCNHead', + in_channels=128, + channels=128, + num_convs=1, + num_classes=46, + in_index=1, + norm_cfg=norm_cfg, + concat_input=False, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + ] + + ) diff --git a/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py b/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py index b8eae174e9..be05796ff6 100644 --- a/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py +++ b/configs/mae/mae-base_upernet_8xb2-amp-160k_ade20k-512x512.py @@ -6,7 +6,7 @@ data_preprocessor = dict(size=crop_size) model = dict( data_preprocessor=data_preprocessor, - pretrained='./pretrain/mae_pretrain_vit_base_mmcls.pth', + # pretrained='./pretrain/mae_pretrain_vit_base_mmcls.pth', backbone=dict( type='MAE', img_size=(512, 512), diff --git a/configs/mask2former/mask2former_r101_8xb2-160k_HOTS_v1-640x480.py b/configs/mask2former/mask2former_r101_8xb2-160k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..ba616d8757 --- /dev/null +++ b/configs/mask2former/mask2former_r101_8xb2-160k_HOTS_v1-640x480.py @@ -0,0 +1,7 @@ +_base_ = ['./mask2former_r50_8xb2-160k_HOTS_v1-640x480.py'] + +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/mask2former/mask2former_r101_8xb2-20k_HOTS_v1-512x512.py b/configs/mask2former/mask2former_r101_8xb2-20k_HOTS_v1-512x512.py new file mode 100644 index 0000000000..12db7aa310 --- /dev/null +++ b/configs/mask2former/mask2former_r101_8xb2-20k_HOTS_v1-512x512.py @@ -0,0 +1,7 @@ +_base_ = ['./mask2former_r50_8xb2-20k_HOTS_v1-512x512.py'] + +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/mask2former/mask2former_r101_8xb2-20k_HOTS_v1-512x512_pretrained_ade20k.py b/configs/mask2former/mask2former_r101_8xb2-20k_HOTS_v1-512x512_pretrained_ade20k.py new file mode 100644 index 0000000000..085332291f --- /dev/null +++ b/configs/mask2former/mask2former_r101_8xb2-20k_HOTS_v1-512x512_pretrained_ade20k.py @@ -0,0 +1,7 @@ +_base_ = ['./mask2former_r50_8xb2-20k_HOTS_v1-512x512.py'] +load_from = "checkpoints/mask2former_r101_8xb2-160k_ade20k-512x512_20221203_233905-b7135890.pth" +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/mask2former/mask2former_r101_8xb2-20k_HOTS_v1-640x480.py b/configs/mask2former/mask2former_r101_8xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..47c59e9bce --- /dev/null +++ b/configs/mask2former/mask2former_r101_8xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,7 @@ +_base_ = ['./mask2former_r50_8xb2-20k_HOTS_v1-640x480.py'] + +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/mask2former/mask2former_r101_8xb2-40k_HOTS_v1-640x480.py b/configs/mask2former/mask2former_r101_8xb2-40k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..2778f8cb38 --- /dev/null +++ b/configs/mask2former/mask2former_r101_8xb2-40k_HOTS_v1-640x480.py @@ -0,0 +1,7 @@ +_base_ = ['./mask2former_r50_8xb2-40k_HOTS_v1-640x480.py'] + +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/mask2former/mask2former_r101_8xb2-80k_HOTS_v1-640x480.py b/configs/mask2former/mask2former_r101_8xb2-80k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..4fa3a16086 --- /dev/null +++ b/configs/mask2former/mask2former_r101_8xb2-80k_HOTS_v1-640x480.py @@ -0,0 +1,7 @@ +_base_ = ['./mask2former_r50_8xb2-80k_HOTS_v1-640x480.py'] + +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/mask2former/mask2former_r50_8xb2-160k_HOTS_v1-640x480.py b/configs/mask2former/mask2former_r50_8xb2-160k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..b1e2b04a2d --- /dev/null +++ b/configs/mask2former/mask2former_r50_8xb2-160k_HOTS_v1-640x480.py @@ -0,0 +1,170 @@ +_base_ = ['../_base_/default_runtime.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/schedules/schedule_160k.py'] + +custom_imports = dict(imports='mmdet.models', allow_failed_imports=False) + +crop_size = (640, 480) +data_preprocessor = dict( + type='SegDataPreProcessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255, + size=crop_size, + test_cfg=dict(size_divisor=32)) +num_classes = 46 +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='ResNet', + depth=50, + deep_stem=False, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=-1, + norm_cfg=dict(type='SyncBN', requires_grad=False), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + decode_head=dict( + type='Mask2FormerHead', + in_channels=[256, 512, 1024, 2048], + strides=[4, 8, 16, 32], + feat_channels=256, + out_channels=256, + num_classes=num_classes, + num_queries=100, + num_transformer_feat_level=3, + align_corners=False, + pixel_decoder=dict( + type='mmdet.MSDeformAttnPixelDecoder', + num_outs=3, + norm_cfg=dict(type='GN', num_groups=32), + act_cfg=dict(type='ReLU'), + encoder=dict( # DeformableDetrTransformerEncoder + num_layers=6, + layer_cfg=dict( # DeformableDetrTransformerEncoderLayer + self_attn_cfg=dict( # MultiScaleDeformableAttention + embed_dims=256, + num_heads=8, + num_levels=3, + num_points=4, + im2col_step=64, + dropout=0.0, + batch_first=True, + norm_cfg=None, + init_cfg=None), + ffn_cfg=dict( + embed_dims=256, + feedforward_channels=1024, + num_fcs=2, + ffn_drop=0.0, + act_cfg=dict(type='ReLU', inplace=True))), + init_cfg=None), + positional_encoding=dict( # SinePositionalEncoding + num_feats=128, normalize=True), + init_cfg=None), + enforce_decoder_input_project=False, + positional_encoding=dict( # SinePositionalEncoding + num_feats=128, normalize=True), + transformer_decoder=dict( # Mask2FormerTransformerDecoder + return_intermediate=True, + num_layers=9, + layer_cfg=dict( # Mask2FormerTransformerDecoderLayer + self_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.0, + proj_drop=0.0, + dropout_layer=None, + batch_first=True), + cross_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.0, + proj_drop=0.0, + dropout_layer=None, + batch_first=True), + ffn_cfg=dict( + embed_dims=256, + feedforward_channels=2048, + num_fcs=2, + act_cfg=dict(type='ReLU', inplace=True), + ffn_drop=0.0, + dropout_layer=None, + add_identity=True)), + init_cfg=None), + loss_cls=dict( + type='mmdet.CrossEntropyLoss', + use_sigmoid=False, + loss_weight=2.0, + reduction='mean', + class_weight=[1.0] * num_classes + [0.1]), + loss_mask=dict( + type='mmdet.CrossEntropyLoss', + use_sigmoid=True, + reduction='mean', + loss_weight=5.0), + loss_dice=dict( + type='mmdet.DiceLoss', + use_sigmoid=True, + activate=True, + reduction='mean', + naive_dice=True, + eps=1.0, + loss_weight=5.0), + train_cfg=dict( + num_points=12544, + oversample_ratio=3.0, + importance_sample_ratio=0.75, + assigner=dict( + type='mmdet.HungarianAssigner', + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + weight=5.0, + use_sigmoid=True), + dict( + type='mmdet.DiceCost', + weight=5.0, + pred_act=True, + eps=1.0) + ]), + sampler=dict(type='mmdet.MaskPseudoSampler'))), + train_cfg=dict(), + test_cfg=dict(mode='whole')) + +# dataset config +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=True), + dict( + type='RandomChoiceResize', + scales=[int(512 * x * 0.1) for x in range(5, 21)], + resize_type='ResizeShortestEdge', + max_size=2048), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs') +] +train_dataloader = dict(batch_size=2, dataset=dict(pipeline=train_pipeline)) + +# optimizer +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +optimizer = dict( + type='AdamW', lr=0.0001, weight_decay=0.05, eps=1e-8, betas=(0.9, 0.999), _delete_=True) +optim_wrapper = dict( + type='OptimWrapper', + optimizer=optimizer, + clip_grad=dict(max_norm=0.01, norm_type=2, _delete_=True), + paramwise_cfg=dict( + custom_keys={ + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi, + }, + norm_decay_mult=0.0)) diff --git a/configs/mask2former/mask2former_r50_8xb2-20k_HOTS_v1-512x512.py b/configs/mask2former/mask2former_r50_8xb2-20k_HOTS_v1-512x512.py new file mode 100644 index 0000000000..15ca2e0a42 --- /dev/null +++ b/configs/mask2former/mask2former_r50_8xb2-20k_HOTS_v1-512x512.py @@ -0,0 +1,200 @@ +_base_ = ['../_base_/default_runtime.py', '../_base_/datasets/hots_v1_640x480.py'] + +custom_imports = dict(imports='mmdet.models', allow_failed_imports=False) + +crop_size = (512, 512) +data_preprocessor = dict( + type='SegDataPreProcessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255, + size=crop_size, + test_cfg=dict(size_divisor=32)) +num_classes = 46 +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='ResNet', + depth=50, + deep_stem=False, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=-1, + norm_cfg=dict(type='SyncBN', requires_grad=False), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + decode_head=dict( + type='Mask2FormerHead', + in_channels=[256, 512, 1024, 2048], + strides=[4, 8, 16, 32], + feat_channels=256, + out_channels=256, + num_classes=num_classes, + num_queries=100, + num_transformer_feat_level=3, + align_corners=False, + pixel_decoder=dict( + type='mmdet.MSDeformAttnPixelDecoder', + num_outs=3, + norm_cfg=dict(type='GN', num_groups=32), + act_cfg=dict(type='ReLU'), + encoder=dict( # DeformableDetrTransformerEncoder + num_layers=6, + layer_cfg=dict( # DeformableDetrTransformerEncoderLayer + self_attn_cfg=dict( # MultiScaleDeformableAttention + embed_dims=256, + num_heads=8, + num_levels=3, + num_points=4, + im2col_step=64, + dropout=0.0, + batch_first=True, + norm_cfg=None, + init_cfg=None), + ffn_cfg=dict( + embed_dims=256, + feedforward_channels=1024, + num_fcs=2, + ffn_drop=0.0, + act_cfg=dict(type='ReLU', inplace=True))), + init_cfg=None), + positional_encoding=dict( # SinePositionalEncoding + num_feats=128, normalize=True), + init_cfg=None), + enforce_decoder_input_project=False, + positional_encoding=dict( # SinePositionalEncoding + num_feats=128, normalize=True), + transformer_decoder=dict( # Mask2FormerTransformerDecoder + return_intermediate=True, + num_layers=9, + layer_cfg=dict( # Mask2FormerTransformerDecoderLayer + self_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.0, + proj_drop=0.0, + dropout_layer=None, + batch_first=True), + cross_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.0, + proj_drop=0.0, + dropout_layer=None, + batch_first=True), + ffn_cfg=dict( + embed_dims=256, + feedforward_channels=2048, + num_fcs=2, + act_cfg=dict(type='ReLU', inplace=True), + ffn_drop=0.0, + dropout_layer=None, + add_identity=True)), + init_cfg=None), + loss_cls=dict( + type='mmdet.CrossEntropyLoss', + use_sigmoid=False, + loss_weight=2.0, + reduction='mean', + class_weight=[1.0] * num_classes + [0.1]), + loss_mask=dict( + type='mmdet.CrossEntropyLoss', + use_sigmoid=True, + reduction='mean', + loss_weight=5.0), + loss_dice=dict( + type='mmdet.DiceLoss', + use_sigmoid=True, + activate=True, + reduction='mean', + naive_dice=True, + eps=1.0, + loss_weight=5.0), + train_cfg=dict( + num_points=12544, + oversample_ratio=3.0, + importance_sample_ratio=0.75, + assigner=dict( + type='mmdet.HungarianAssigner', + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + weight=5.0, + use_sigmoid=True), + dict( + type='mmdet.DiceCost', + weight=5.0, + pred_act=True, + eps=1.0) + ]), + sampler=dict(type='mmdet.MaskPseudoSampler'))), + train_cfg=dict(), + test_cfg=dict(mode='whole')) + +# dataset config +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=True), + dict( + type='RandomChoiceResize', + scales=[int(512 * x * 0.1) for x in range(5, 21)], + resize_type='ResizeShortestEdge', + max_size=2048), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs') +] +train_dataloader = dict(batch_size=2, dataset=dict(pipeline=train_pipeline)) + +# optimizer +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +optimizer = dict( + type='AdamW', lr=0.0001, weight_decay=0.05, eps=1e-8, betas=(0.9, 0.999)) +optim_wrapper = dict( + type='OptimWrapper', + optimizer=optimizer, + clip_grad=dict(max_norm=0.01, norm_type=2), + paramwise_cfg=dict( + custom_keys={ + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi, + }, + norm_decay_mult=0.0)) +# learning policy +param_scheduler = [ + dict( + type='PolyLR', + eta_min=0, + power=0.9, + begin=0, + end=20000, + by_epoch=False) +] + +# training schedule for 160k +train_cfg = dict( + type='IterBasedTrainLoop', max_iters=20000, val_interval=100) +val_cfg = dict(type='ValLoop') +test_cfg = dict(type='TestLoop') +default_hooks = dict( + timer=dict(type='IterTimerHook'), + logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), + param_scheduler=dict(type='ParamSchedulerHook'), + checkpoint=dict( + type='CheckpointHook', by_epoch=False, interval=2000, + save_best='mIoU'), + sampler_seed=dict(type='DistSamplerSeedHook'), + visualization=dict(type='SegVisualizationHook')) + +# Default setting for scaling LR automatically +# - `enable` means enable scaling LR automatically +# or not by default. +# - `base_batch_size` = (8 GPUs) x (2 samples per GPU). +auto_scale_lr = dict(enable=False, base_batch_size=16) \ No newline at end of file diff --git a/configs/mask2former/mask2former_r50_8xb2-20k_HOTS_v1-512x512_pretrained_ade20k.py b/configs/mask2former/mask2former_r50_8xb2-20k_HOTS_v1-512x512_pretrained_ade20k.py new file mode 100644 index 0000000000..571cbe5f1c --- /dev/null +++ b/configs/mask2former/mask2former_r50_8xb2-20k_HOTS_v1-512x512_pretrained_ade20k.py @@ -0,0 +1,201 @@ +_base_ = ['../_base_/default_runtime.py', '../_base_/datasets/hots_v1_640x480.py'] + +custom_imports = dict(imports='mmdet.models', allow_failed_imports=False) + +crop_size = (512, 512) +data_preprocessor = dict( + type='SegDataPreProcessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255, + size=crop_size, + test_cfg=dict(size_divisor=32)) +num_classes = 46 +load_from = "checkpoints/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth" +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='ResNet', + depth=50, + deep_stem=False, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=-1, + norm_cfg=dict(type='SyncBN', requires_grad=False), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + decode_head=dict( + type='Mask2FormerHead', + in_channels=[256, 512, 1024, 2048], + strides=[4, 8, 16, 32], + feat_channels=256, + out_channels=256, + num_classes=num_classes, + num_queries=100, + num_transformer_feat_level=3, + align_corners=False, + pixel_decoder=dict( + type='mmdet.MSDeformAttnPixelDecoder', + num_outs=3, + norm_cfg=dict(type='GN', num_groups=32), + act_cfg=dict(type='ReLU'), + encoder=dict( # DeformableDetrTransformerEncoder + num_layers=6, + layer_cfg=dict( # DeformableDetrTransformerEncoderLayer + self_attn_cfg=dict( # MultiScaleDeformableAttention + embed_dims=256, + num_heads=8, + num_levels=3, + num_points=4, + im2col_step=64, + dropout=0.0, + batch_first=True, + norm_cfg=None, + init_cfg=None), + ffn_cfg=dict( + embed_dims=256, + feedforward_channels=1024, + num_fcs=2, + ffn_drop=0.0, + act_cfg=dict(type='ReLU', inplace=True))), + init_cfg=None), + positional_encoding=dict( # SinePositionalEncoding + num_feats=128, normalize=True), + init_cfg=None), + enforce_decoder_input_project=False, + positional_encoding=dict( # SinePositionalEncoding + num_feats=128, normalize=True), + transformer_decoder=dict( # Mask2FormerTransformerDecoder + return_intermediate=True, + num_layers=9, + layer_cfg=dict( # Mask2FormerTransformerDecoderLayer + self_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.0, + proj_drop=0.0, + dropout_layer=None, + batch_first=True), + cross_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.0, + proj_drop=0.0, + dropout_layer=None, + batch_first=True), + ffn_cfg=dict( + embed_dims=256, + feedforward_channels=2048, + num_fcs=2, + act_cfg=dict(type='ReLU', inplace=True), + ffn_drop=0.0, + dropout_layer=None, + add_identity=True)), + init_cfg=None), + loss_cls=dict( + type='mmdet.CrossEntropyLoss', + use_sigmoid=False, + loss_weight=2.0, + reduction='mean', + class_weight=[1.0] * num_classes + [0.1]), + loss_mask=dict( + type='mmdet.CrossEntropyLoss', + use_sigmoid=True, + reduction='mean', + loss_weight=5.0), + loss_dice=dict( + type='mmdet.DiceLoss', + use_sigmoid=True, + activate=True, + reduction='mean', + naive_dice=True, + eps=1.0, + loss_weight=5.0), + train_cfg=dict( + num_points=12544, + oversample_ratio=3.0, + importance_sample_ratio=0.75, + assigner=dict( + type='mmdet.HungarianAssigner', + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + weight=5.0, + use_sigmoid=True), + dict( + type='mmdet.DiceCost', + weight=5.0, + pred_act=True, + eps=1.0) + ]), + sampler=dict(type='mmdet.MaskPseudoSampler'))), + train_cfg=dict(), + test_cfg=dict(mode='whole')) + +# dataset config +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=True), + dict( + type='RandomChoiceResize', + scales=[int(512 * x * 0.1) for x in range(5, 21)], + resize_type='ResizeShortestEdge', + max_size=2048), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs') +] +train_dataloader = dict(batch_size=2, dataset=dict(pipeline=train_pipeline)) + +# optimizer +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +optimizer = dict( + type='AdamW', lr=0.0001, weight_decay=0.05, eps=1e-8, betas=(0.9, 0.999)) +optim_wrapper = dict( + type='OptimWrapper', + optimizer=optimizer, + clip_grad=dict(max_norm=0.01, norm_type=2), + paramwise_cfg=dict( + custom_keys={ + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi, + }, + norm_decay_mult=0.0)) +# learning policy +param_scheduler = [ + dict( + type='PolyLR', + eta_min=0, + power=0.9, + begin=0, + end=20000, + by_epoch=False) +] + +# training schedule for 160k +train_cfg = dict( + type='IterBasedTrainLoop', max_iters=20000, val_interval=2000) +val_cfg = dict(type='ValLoop') +test_cfg = dict(type='TestLoop') +default_hooks = dict( + timer=dict(type='IterTimerHook'), + logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), + param_scheduler=dict(type='ParamSchedulerHook'), + checkpoint=dict( + type='CheckpointHook', by_epoch=False, interval=2000, + save_best='mIoU'), + sampler_seed=dict(type='DistSamplerSeedHook'), + visualization=dict(type='SegVisualizationHook')) + +# Default setting for scaling LR automatically +# - `enable` means enable scaling LR automatically +# or not by default. +# - `base_batch_size` = (8 GPUs) x (2 samples per GPU). +auto_scale_lr = dict(enable=False, base_batch_size=16) \ No newline at end of file diff --git a/configs/mask2former/mask2former_r50_8xb2-20k_HOTS_v1-640x480.py b/configs/mask2former/mask2former_r50_8xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..5b46d82fa0 --- /dev/null +++ b/configs/mask2former/mask2former_r50_8xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,200 @@ +_base_ = ['../_base_/default_runtime.py', '../_base_/datasets/hots_v1_640x480.py'] + +custom_imports = dict(imports='mmdet.models', allow_failed_imports=False) + +crop_size = (640, 480) +data_preprocessor = dict( + type='SegDataPreProcessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255, + size=crop_size, + test_cfg=dict(size_divisor=32)) +num_classes = 46 +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='ResNet', + depth=50, + deep_stem=False, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=-1, + norm_cfg=dict(type='SyncBN', requires_grad=False), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + decode_head=dict( + type='Mask2FormerHead', + in_channels=[256, 512, 1024, 2048], + strides=[4, 8, 16, 32], + feat_channels=256, + out_channels=256, + num_classes=num_classes, + num_queries=100, + num_transformer_feat_level=3, + align_corners=False, + pixel_decoder=dict( + type='mmdet.MSDeformAttnPixelDecoder', + num_outs=3, + norm_cfg=dict(type='GN', num_groups=32), + act_cfg=dict(type='ReLU'), + encoder=dict( # DeformableDetrTransformerEncoder + num_layers=6, + layer_cfg=dict( # DeformableDetrTransformerEncoderLayer + self_attn_cfg=dict( # MultiScaleDeformableAttention + embed_dims=256, + num_heads=8, + num_levels=3, + num_points=4, + im2col_step=64, + dropout=0.0, + batch_first=True, + norm_cfg=None, + init_cfg=None), + ffn_cfg=dict( + embed_dims=256, + feedforward_channels=1024, + num_fcs=2, + ffn_drop=0.0, + act_cfg=dict(type='ReLU', inplace=True))), + init_cfg=None), + positional_encoding=dict( # SinePositionalEncoding + num_feats=128, normalize=True), + init_cfg=None), + enforce_decoder_input_project=False, + positional_encoding=dict( # SinePositionalEncoding + num_feats=128, normalize=True), + transformer_decoder=dict( # Mask2FormerTransformerDecoder + return_intermediate=True, + num_layers=9, + layer_cfg=dict( # Mask2FormerTransformerDecoderLayer + self_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.0, + proj_drop=0.0, + dropout_layer=None, + batch_first=True), + cross_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.0, + proj_drop=0.0, + dropout_layer=None, + batch_first=True), + ffn_cfg=dict( + embed_dims=256, + feedforward_channels=2048, + num_fcs=2, + act_cfg=dict(type='ReLU', inplace=True), + ffn_drop=0.0, + dropout_layer=None, + add_identity=True)), + init_cfg=None), + loss_cls=dict( + type='mmdet.CrossEntropyLoss', + use_sigmoid=False, + loss_weight=2.0, + reduction='mean', + class_weight=[1.0] * num_classes + [0.1]), + loss_mask=dict( + type='mmdet.CrossEntropyLoss', + use_sigmoid=True, + reduction='mean', + loss_weight=5.0), + loss_dice=dict( + type='mmdet.DiceLoss', + use_sigmoid=True, + activate=True, + reduction='mean', + naive_dice=True, + eps=1.0, + loss_weight=5.0), + train_cfg=dict( + num_points=12544, + oversample_ratio=3.0, + importance_sample_ratio=0.75, + assigner=dict( + type='mmdet.HungarianAssigner', + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + weight=5.0, + use_sigmoid=True), + dict( + type='mmdet.DiceCost', + weight=5.0, + pred_act=True, + eps=1.0) + ]), + sampler=dict(type='mmdet.MaskPseudoSampler'))), + train_cfg=dict(), + test_cfg=dict(mode='whole')) + +# dataset config +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=True), + dict( + type='RandomChoiceResize', + scales=[int(512 * x * 0.1) for x in range(5, 21)], + resize_type='ResizeShortestEdge', + max_size=2048), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs') +] +train_dataloader = dict(batch_size=2, dataset=dict(pipeline=train_pipeline)) + +# optimizer +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +optimizer = dict( + type='AdamW', lr=0.001, weight_decay=0.05, eps=1e-8, betas=(0.9, 0.999)) +optim_wrapper = dict( + type='OptimWrapper', + optimizer=optimizer, + clip_grad=dict(max_norm=0.01, norm_type=2), + paramwise_cfg=dict( + custom_keys={ + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi, + }, + norm_decay_mult=0.0)) +# learning policy +param_scheduler = [ + dict( + type='PolyLR', + eta_min=0, + power=0.9, + begin=0, + end=20000, + by_epoch=False) +] + +# training schedule for 160k +train_cfg = dict( + type='IterBasedTrainLoop', max_iters=20000, val_interval=2000) +val_cfg = dict(type='ValLoop') +test_cfg = dict(type='TestLoop') +default_hooks = dict( + timer=dict(type='IterTimerHook'), + logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), + param_scheduler=dict(type='ParamSchedulerHook'), + checkpoint=dict( + type='CheckpointHook', by_epoch=False, interval=2000, + save_best='mIoU'), + sampler_seed=dict(type='DistSamplerSeedHook'), + visualization=dict(type='SegVisualizationHook')) + +# Default setting for scaling LR automatically +# - `enable` means enable scaling LR automatically +# or not by default. +# - `base_batch_size` = (8 GPUs) x (2 samples per GPU). +auto_scale_lr = dict(enable=False, base_batch_size=16) \ No newline at end of file diff --git a/configs/mask2former/mask2former_r50_8xb2-40k_HOTS_v1-640x480.py b/configs/mask2former/mask2former_r50_8xb2-40k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..e091fb9196 --- /dev/null +++ b/configs/mask2former/mask2former_r50_8xb2-40k_HOTS_v1-640x480.py @@ -0,0 +1,170 @@ +_base_ = ['../_base_/default_runtime.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/schedules/schedule_40k.py'] + +custom_imports = dict(imports='mmdet.models', allow_failed_imports=False) + +crop_size = (640, 480) +data_preprocessor = dict( + type='SegDataPreProcessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255, + size=crop_size, + test_cfg=dict(size_divisor=32)) +num_classes = 46 +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='ResNet', + depth=50, + deep_stem=False, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=-1, + norm_cfg=dict(type='SyncBN', requires_grad=False), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + decode_head=dict( + type='Mask2FormerHead', + in_channels=[256, 512, 1024, 2048], + strides=[4, 8, 16, 32], + feat_channels=256, + out_channels=256, + num_classes=num_classes, + num_queries=100, + num_transformer_feat_level=3, + align_corners=False, + pixel_decoder=dict( + type='mmdet.MSDeformAttnPixelDecoder', + num_outs=3, + norm_cfg=dict(type='GN', num_groups=32), + act_cfg=dict(type='ReLU'), + encoder=dict( # DeformableDetrTransformerEncoder + num_layers=6, + layer_cfg=dict( # DeformableDetrTransformerEncoderLayer + self_attn_cfg=dict( # MultiScaleDeformableAttention + embed_dims=256, + num_heads=8, + num_levels=3, + num_points=4, + im2col_step=64, + dropout=0.0, + batch_first=True, + norm_cfg=None, + init_cfg=None), + ffn_cfg=dict( + embed_dims=256, + feedforward_channels=1024, + num_fcs=2, + ffn_drop=0.0, + act_cfg=dict(type='ReLU', inplace=True))), + init_cfg=None), + positional_encoding=dict( # SinePositionalEncoding + num_feats=128, normalize=True), + init_cfg=None), + enforce_decoder_input_project=False, + positional_encoding=dict( # SinePositionalEncoding + num_feats=128, normalize=True), + transformer_decoder=dict( # Mask2FormerTransformerDecoder + return_intermediate=True, + num_layers=9, + layer_cfg=dict( # Mask2FormerTransformerDecoderLayer + self_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.0, + proj_drop=0.0, + dropout_layer=None, + batch_first=True), + cross_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.0, + proj_drop=0.0, + dropout_layer=None, + batch_first=True), + ffn_cfg=dict( + embed_dims=256, + feedforward_channels=2048, + num_fcs=2, + act_cfg=dict(type='ReLU', inplace=True), + ffn_drop=0.0, + dropout_layer=None, + add_identity=True)), + init_cfg=None), + loss_cls=dict( + type='mmdet.CrossEntropyLoss', + use_sigmoid=False, + loss_weight=2.0, + reduction='mean', + class_weight=[1.0] * num_classes + [0.1]), + loss_mask=dict( + type='mmdet.CrossEntropyLoss', + use_sigmoid=True, + reduction='mean', + loss_weight=5.0), + loss_dice=dict( + type='mmdet.DiceLoss', + use_sigmoid=True, + activate=True, + reduction='mean', + naive_dice=True, + eps=1.0, + loss_weight=5.0), + train_cfg=dict( + num_points=12544, + oversample_ratio=3.0, + importance_sample_ratio=0.75, + assigner=dict( + type='mmdet.HungarianAssigner', + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + weight=5.0, + use_sigmoid=True), + dict( + type='mmdet.DiceCost', + weight=5.0, + pred_act=True, + eps=1.0) + ]), + sampler=dict(type='mmdet.MaskPseudoSampler'))), + train_cfg=dict(), + test_cfg=dict(mode='whole')) + +# dataset config +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=True), + dict( + type='RandomChoiceResize', + scales=[int(512 * x * 0.1) for x in range(5, 21)], + resize_type='ResizeShortestEdge', + max_size=2048), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs') +] +train_dataloader = dict(batch_size=2, dataset=dict(pipeline=train_pipeline)) + +# optimizer +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +optimizer = dict( + type='AdamW', lr=0.0001, weight_decay=0.05, eps=1e-8, betas=(0.9, 0.999), _delete_=True) +optim_wrapper = dict( + type='OptimWrapper', + optimizer=optimizer, + clip_grad=dict(max_norm=0.01, norm_type=2, _delete_=True), + paramwise_cfg=dict( + custom_keys={ + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi, + }, + norm_decay_mult=0.0)) diff --git a/configs/mask2former/mask2former_r50_8xb2-80k_HOTS_v1-640x480.py b/configs/mask2former/mask2former_r50_8xb2-80k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..eb9c1bca38 --- /dev/null +++ b/configs/mask2former/mask2former_r50_8xb2-80k_HOTS_v1-640x480.py @@ -0,0 +1,170 @@ +_base_ = ['../_base_/default_runtime.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/schedules/schedule_80k.py'] + +custom_imports = dict(imports='mmdet.models', allow_failed_imports=False) + +crop_size = (640, 480) +data_preprocessor = dict( + type='SegDataPreProcessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255, + size=crop_size, + test_cfg=dict(size_divisor=32)) +num_classes = 46 +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='ResNet', + depth=50, + deep_stem=False, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=-1, + norm_cfg=dict(type='SyncBN', requires_grad=False), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + decode_head=dict( + type='Mask2FormerHead', + in_channels=[256, 512, 1024, 2048], + strides=[4, 8, 16, 32], + feat_channels=256, + out_channels=256, + num_classes=num_classes, + num_queries=100, + num_transformer_feat_level=3, + align_corners=False, + pixel_decoder=dict( + type='mmdet.MSDeformAttnPixelDecoder', + num_outs=3, + norm_cfg=dict(type='GN', num_groups=32), + act_cfg=dict(type='ReLU'), + encoder=dict( # DeformableDetrTransformerEncoder + num_layers=6, + layer_cfg=dict( # DeformableDetrTransformerEncoderLayer + self_attn_cfg=dict( # MultiScaleDeformableAttention + embed_dims=256, + num_heads=8, + num_levels=3, + num_points=4, + im2col_step=64, + dropout=0.0, + batch_first=True, + norm_cfg=None, + init_cfg=None), + ffn_cfg=dict( + embed_dims=256, + feedforward_channels=1024, + num_fcs=2, + ffn_drop=0.0, + act_cfg=dict(type='ReLU', inplace=True))), + init_cfg=None), + positional_encoding=dict( # SinePositionalEncoding + num_feats=128, normalize=True), + init_cfg=None), + enforce_decoder_input_project=False, + positional_encoding=dict( # SinePositionalEncoding + num_feats=128, normalize=True), + transformer_decoder=dict( # Mask2FormerTransformerDecoder + return_intermediate=True, + num_layers=9, + layer_cfg=dict( # Mask2FormerTransformerDecoderLayer + self_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.0, + proj_drop=0.0, + dropout_layer=None, + batch_first=True), + cross_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.0, + proj_drop=0.0, + dropout_layer=None, + batch_first=True), + ffn_cfg=dict( + embed_dims=256, + feedforward_channels=2048, + num_fcs=2, + act_cfg=dict(type='ReLU', inplace=True), + ffn_drop=0.0, + dropout_layer=None, + add_identity=True)), + init_cfg=None), + loss_cls=dict( + type='mmdet.CrossEntropyLoss', + use_sigmoid=False, + loss_weight=2.0, + reduction='mean', + class_weight=[1.0] * num_classes + [0.1]), + loss_mask=dict( + type='mmdet.CrossEntropyLoss', + use_sigmoid=True, + reduction='mean', + loss_weight=5.0), + loss_dice=dict( + type='mmdet.DiceLoss', + use_sigmoid=True, + activate=True, + reduction='mean', + naive_dice=True, + eps=1.0, + loss_weight=5.0), + train_cfg=dict( + num_points=12544, + oversample_ratio=3.0, + importance_sample_ratio=0.75, + assigner=dict( + type='mmdet.HungarianAssigner', + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + weight=5.0, + use_sigmoid=True), + dict( + type='mmdet.DiceCost', + weight=5.0, + pred_act=True, + eps=1.0) + ]), + sampler=dict(type='mmdet.MaskPseudoSampler'))), + train_cfg=dict(), + test_cfg=dict(mode='whole')) + +# dataset config +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=True), + dict( + type='RandomChoiceResize', + scales=[int(512 * x * 0.1) for x in range(5, 21)], + resize_type='ResizeShortestEdge', + max_size=2048), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs') +] +train_dataloader = dict(batch_size=2, dataset=dict(pipeline=train_pipeline)) + +# optimizer +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +optimizer = dict( + type='AdamW', lr=0.0001, weight_decay=0.05, eps=1e-8, betas=(0.9, 0.999), _delete_=True) +optim_wrapper = dict( + type='OptimWrapper', + optimizer=optimizer, + clip_grad=dict(max_norm=0.01, norm_type=2, _delete_=True), + paramwise_cfg=dict( + custom_keys={ + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi, + }, + norm_decay_mult=0.0)) diff --git a/configs/mask2former/mask2former_swin-s_8xb2-160k_HOTS_v1-640x480.py b/configs/mask2former/mask2former_swin-s_8xb2-160k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..6812ecc108 --- /dev/null +++ b/configs/mask2former/mask2former_swin-s_8xb2-160k_HOTS_v1-640x480.py @@ -0,0 +1,37 @@ +_base_ = ['./mask2former_swin-t_8xb2-160k_HOTS_v1-640x480.py'] +pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth' # noqa + +depths = [2, 2, 18, 2] +model = dict( + backbone=dict( + depths=depths, init_cfg=dict(type='Pretrained', + checkpoint=pretrained))) + +# set all layers in backbone to lr_mult=0.1 +# set all norm layers, position_embeding, +# query_embeding, level_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0) +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'absolute_pos_embed': backbone_embed_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optim_wrapper = dict( + paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0)) diff --git a/configs/mask2former/mask2former_swin-s_8xb2-20k_HOTS_v1-512x512.py b/configs/mask2former/mask2former_swin-s_8xb2-20k_HOTS_v1-512x512.py new file mode 100644 index 0000000000..e2249cccd4 --- /dev/null +++ b/configs/mask2former/mask2former_swin-s_8xb2-20k_HOTS_v1-512x512.py @@ -0,0 +1,37 @@ +_base_ = ['./mask2former_swin-t_8xb2-20k_HOTS_v1-512x512.py'] +pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth' # noqa + +depths = [2, 2, 18, 2] +model = dict( + backbone=dict( + depths=depths, init_cfg=dict(type='Pretrained', + checkpoint=pretrained))) + +# set all layers in backbone to lr_mult=0.1 +# set all norm layers, position_embeding, +# query_embeding, level_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0) +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'absolute_pos_embed': backbone_embed_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optim_wrapper = dict( + paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0)) diff --git a/configs/mask2former/mask2former_swin-s_8xb2-20k_HOTS_v1-640x480.py b/configs/mask2former/mask2former_swin-s_8xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..9a185dddb4 --- /dev/null +++ b/configs/mask2former/mask2former_swin-s_8xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,37 @@ +_base_ = ['./mask2former_swin-t_8xb2-20k_HOTS_v1-640x480.py'] +pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth' # noqa + +depths = [2, 2, 18, 2] +model = dict( + backbone=dict( + depths=depths, init_cfg=dict(type='Pretrained', + checkpoint=pretrained))) + +# set all layers in backbone to lr_mult=0.1 +# set all norm layers, position_embeding, +# query_embeding, level_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0) +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'absolute_pos_embed': backbone_embed_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optim_wrapper = dict( + paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0)) diff --git a/configs/mask2former/mask2former_swin-s_8xb2-40k_HOTS_v1-640x480.py b/configs/mask2former/mask2former_swin-s_8xb2-40k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..6e09d6dfae --- /dev/null +++ b/configs/mask2former/mask2former_swin-s_8xb2-40k_HOTS_v1-640x480.py @@ -0,0 +1,37 @@ +_base_ = ['./mask2former_swin-t_8xb2-40k_HOTS_v1-640x480.py'] +pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth' # noqa + +depths = [2, 2, 18, 2] +model = dict( + backbone=dict( + depths=depths, init_cfg=dict(type='Pretrained', + checkpoint=pretrained))) + +# set all layers in backbone to lr_mult=0.1 +# set all norm layers, position_embeding, +# query_embeding, level_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0) +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'absolute_pos_embed': backbone_embed_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optim_wrapper = dict( + paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0)) diff --git a/configs/mask2former/mask2former_swin-s_8xb2-80k_HOTS_v1-640x480.py b/configs/mask2former/mask2former_swin-s_8xb2-80k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..e781ee4ca1 --- /dev/null +++ b/configs/mask2former/mask2former_swin-s_8xb2-80k_HOTS_v1-640x480.py @@ -0,0 +1,37 @@ +_base_ = ['./mask2former_swin-t_8xb2-80k_HOTS_v1-640x480.py'] +pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth' # noqa + +depths = [2, 2, 18, 2] +model = dict( + backbone=dict( + depths=depths, init_cfg=dict(type='Pretrained', + checkpoint=pretrained))) + +# set all layers in backbone to lr_mult=0.1 +# set all norm layers, position_embeding, +# query_embeding, level_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0) +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'absolute_pos_embed': backbone_embed_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optim_wrapper = dict( + paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0)) diff --git a/configs/mask2former/mask2former_swin-t_8xb2-160k_HOTS_v1-640x480.py b/configs/mask2former/mask2former_swin-t_8xb2-160k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..0973d234dc --- /dev/null +++ b/configs/mask2former/mask2former_swin-t_8xb2-160k_HOTS_v1-640x480.py @@ -0,0 +1,52 @@ +_base_ = ['./mask2former_r50_8xb2-160k_HOTS_v1-640x480.py'] +pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' # noqa +depths = [2, 2, 6, 2] +model = dict( + backbone=dict( + _delete_=True, + type='SwinTransformer', + embed_dims=96, + depths=depths, + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.3, + patch_norm=True, + out_indices=(0, 1, 2, 3), + with_cp=False, + frozen_stages=-1, + init_cfg=dict(type='Pretrained', checkpoint=pretrained)), + decode_head=dict(in_channels=[96, 192, 384, 768])) + +# set all layers in backbone to lr_mult=0.1 +# set all norm layers, position_embeding, +# query_embeding, level_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0) +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'absolute_pos_embed': backbone_embed_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optim_wrapper = dict( + paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0)) diff --git a/configs/mask2former/mask2former_swin-t_8xb2-20k_HOTS_v1-512x512.py b/configs/mask2former/mask2former_swin-t_8xb2-20k_HOTS_v1-512x512.py new file mode 100644 index 0000000000..86e009f361 --- /dev/null +++ b/configs/mask2former/mask2former_swin-t_8xb2-20k_HOTS_v1-512x512.py @@ -0,0 +1,52 @@ +_base_ = ['./mask2former_r50_8xb2-20k_HOTS_v1-512x512.py'] +pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' # noqa +depths = [2, 2, 6, 2] +model = dict( + backbone=dict( + _delete_=True, + type='SwinTransformer', + embed_dims=96, + depths=depths, + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.3, + patch_norm=True, + out_indices=(0, 1, 2, 3), + with_cp=False, + frozen_stages=-1, + init_cfg=dict(type='Pretrained', checkpoint=pretrained)), + decode_head=dict(in_channels=[96, 192, 384, 768])) + +# set all layers in backbone to lr_mult=0.1 +# set all norm layers, position_embeding, +# query_embeding, level_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0) +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'absolute_pos_embed': backbone_embed_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optim_wrapper = dict( + paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0)) diff --git a/configs/mask2former/mask2former_swin-t_8xb2-20k_HOTS_v1-512x512_pretrained_ade20k.py b/configs/mask2former/mask2former_swin-t_8xb2-20k_HOTS_v1-512x512_pretrained_ade20k.py new file mode 100644 index 0000000000..e68406b08e --- /dev/null +++ b/configs/mask2former/mask2former_swin-t_8xb2-20k_HOTS_v1-512x512_pretrained_ade20k.py @@ -0,0 +1,53 @@ +_base_ = ['./mask2former_r50_8xb2-20k_HOTS_v1-512x512.py'] +pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' # noqa +depths = [2, 2, 6, 2] +load_from = "checkpoints/mask2former_swin-t_8xb2-160k_ade20k-512x512_20221203_234230-7d64e5dd.pth" +model = dict( + backbone=dict( + _delete_=True, + type='SwinTransformer', + embed_dims=96, + depths=depths, + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.3, + patch_norm=True, + out_indices=(0, 1, 2, 3), + with_cp=False, + frozen_stages=-1, + init_cfg=dict(type='Pretrained', checkpoint=pretrained)), + decode_head=dict(in_channels=[96, 192, 384, 768])) + +# set all layers in backbone to lr_mult=0.1 +# set all norm layers, position_embeding, +# query_embeding, level_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0) +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'absolute_pos_embed': backbone_embed_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optim_wrapper = dict( + paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0)) diff --git a/configs/mask2former/mask2former_swin-t_8xb2-20k_HOTS_v1-640x480.py b/configs/mask2former/mask2former_swin-t_8xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..5894008c91 --- /dev/null +++ b/configs/mask2former/mask2former_swin-t_8xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,52 @@ +_base_ = ['./mask2former_r50_8xb2-20k_HOTS_v1-640x480.py'] +pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' # noqa +depths = [2, 2, 6, 2] +model = dict( + backbone=dict( + _delete_=True, + type='SwinTransformer', + embed_dims=96, + depths=depths, + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.3, + patch_norm=True, + out_indices=(0, 1, 2, 3), + with_cp=False, + frozen_stages=-1, + init_cfg=dict(type='Pretrained', checkpoint=pretrained)), + decode_head=dict(in_channels=[96, 192, 384, 768])) + +# set all layers in backbone to lr_mult=0.1 +# set all norm layers, position_embeding, +# query_embeding, level_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0) +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'absolute_pos_embed': backbone_embed_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optim_wrapper = dict( + paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0)) diff --git a/configs/mask2former/mask2former_swin-t_8xb2-40k_HOTS_v1-640x480.py b/configs/mask2former/mask2former_swin-t_8xb2-40k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..c36980c10d --- /dev/null +++ b/configs/mask2former/mask2former_swin-t_8xb2-40k_HOTS_v1-640x480.py @@ -0,0 +1,52 @@ +_base_ = ['./mask2former_r50_8xb2-40k_HOTS_v1-640x480.py'] +pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' # noqa +depths = [2, 2, 6, 2] +model = dict( + backbone=dict( + _delete_=True, + type='SwinTransformer', + embed_dims=96, + depths=depths, + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.3, + patch_norm=True, + out_indices=(0, 1, 2, 3), + with_cp=False, + frozen_stages=-1, + init_cfg=dict(type='Pretrained', checkpoint=pretrained)), + decode_head=dict(in_channels=[96, 192, 384, 768])) + +# set all layers in backbone to lr_mult=0.1 +# set all norm layers, position_embeding, +# query_embeding, level_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0) +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'absolute_pos_embed': backbone_embed_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optim_wrapper = dict( + paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0)) diff --git a/configs/mask2former/mask2former_swin-t_8xb2-80k_HOTS_v1-640x480.py b/configs/mask2former/mask2former_swin-t_8xb2-80k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..f34b8cff19 --- /dev/null +++ b/configs/mask2former/mask2former_swin-t_8xb2-80k_HOTS_v1-640x480.py @@ -0,0 +1,52 @@ +_base_ = ['./mask2former_r50_8xb2-80k_HOTS_v1-640x480.py'] +pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' # noqa +depths = [2, 2, 6, 2] +model = dict( + backbone=dict( + _delete_=True, + type='SwinTransformer', + embed_dims=96, + depths=depths, + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.3, + patch_norm=True, + out_indices=(0, 1, 2, 3), + with_cp=False, + frozen_stages=-1, + init_cfg=dict(type='Pretrained', checkpoint=pretrained)), + decode_head=dict(in_channels=[96, 192, 384, 768])) + +# set all layers in backbone to lr_mult=0.1 +# set all norm layers, position_embeding, +# query_embeding, level_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0) +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'absolute_pos_embed': backbone_embed_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optim_wrapper = dict( + paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0)) diff --git a/configs/maskformer/maskformer_r101-d32_8xb2-20k_HOTS_v1-512x512.py b/configs/maskformer/maskformer_r101-d32_8xb2-20k_HOTS_v1-512x512.py new file mode 100644 index 0000000000..ac98393158 --- /dev/null +++ b/configs/maskformer/maskformer_r101-d32_8xb2-20k_HOTS_v1-512x512.py @@ -0,0 +1,7 @@ +_base_ = ['./maskformer_r50-d32_8xb2-20k_HOTS_v1-512x512.py'] + +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/maskformer/maskformer_r50-d32_8xb2-1k_HOTS_v1-512x512-pre-ade20k.py b/configs/maskformer/maskformer_r50-d32_8xb2-1k_HOTS_v1-512x512-pre-ade20k.py new file mode 100644 index 0000000000..d4b492f689 --- /dev/null +++ b/configs/maskformer/maskformer_r50-d32_8xb2-1k_HOTS_v1-512x512-pre-ade20k.py @@ -0,0 +1,165 @@ +_base_ = [ + '../_base_/datasets/hots_v1_640x480.py', '../_base_/default_runtime.py' +] +norm_cfg = dict(type='SyncBN', requires_grad=True) +crop_size = (512, 512) +data_preprocessor = dict( + type='SegDataPreProcessor', + size=crop_size, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +# model_cfg +num_classes = 46 +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512/maskformer_r50-d32_8xb2-160k_ade20k-512x512_20221030_182724-3a9cfe45.pth' +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 1, 1), + strides=(1, 2, 2, 2), + norm_cfg=norm_cfg, + norm_eval=True, + style='pytorch', + contract_dilation=True, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + decode_head=dict( + type='MaskFormerHead', + in_channels=[256, 512, 1024, + 2048], # input channels of pixel_decoder modules + feat_channels=256, + in_index=[0, 1, 2, 3], + num_classes=num_classes, + out_channels=256, + num_queries=100, + pixel_decoder=dict( + type='mmdet.PixelDecoder', + norm_cfg=dict(type='GN', num_groups=32), + act_cfg=dict(type='ReLU')), + enforce_decoder_input_project=False, + positional_encoding=dict( # SinePositionalEncoding + num_feats=128, normalize=True), + transformer_decoder=dict( # DetrTransformerDecoder + return_intermediate=True, + num_layers=6, + layer_cfg=dict( # DetrTransformerDecoderLayer + self_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.1, + proj_drop=0.1, + dropout_layer=None, + batch_first=True), + cross_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.1, + proj_drop=0.1, + dropout_layer=None, + batch_first=True), + ffn_cfg=dict( + embed_dims=256, + feedforward_channels=2048, + num_fcs=2, + act_cfg=dict(type='ReLU', inplace=True), + ffn_drop=0.1, + dropout_layer=None, + add_identity=True)), + init_cfg=None), + loss_cls=dict( + type='mmdet.CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0, + reduction='mean', + class_weight=[1.0] * num_classes + [0.1]), + loss_mask=dict( + type='mmdet.FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + reduction='mean', + loss_weight=20.0), + loss_dice=dict( + type='mmdet.DiceLoss', + use_sigmoid=True, + activate=True, + reduction='mean', + naive_dice=True, + eps=1.0, + loss_weight=1.0), + train_cfg=dict( + assigner=dict( + type='mmdet.HungarianAssigner', + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + type='mmdet.FocalLossCost', + weight=20.0, + binary_input=True), + dict( + type='mmdet.DiceCost', + weight=1.0, + pred_act=True, + eps=1.0) + ]), + sampler=dict(type='mmdet.MaskPseudoSampler'))), + # training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole'), +) + +# dataset config +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=True), + dict( + type='RandomChoiceResize', + scales=[int(512 * x * 0.1) for x in range(5, 21)], + resize_type='ResizeShortestEdge', + max_size=2048), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs') +] +train_dataloader = dict(batch_size=2, dataset=dict(pipeline=train_pipeline)) + +# optimizer +optimizer = dict( + type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.0001) +optim_wrapper = dict( + type='OptimWrapper', + optimizer=optimizer, + clip_grad=dict(max_norm=0.01, norm_type=2), + paramwise_cfg=dict(custom_keys={ + 'backbone': dict(lr_mult=0.1), + })) +# learning policy +param_scheduler = [ + dict( + type='PolyLR', + eta_min=0, + power=0.9, + begin=0, + end=1000, + by_epoch=False) +] +train_cfg = dict( + type='IterBasedTrainLoop', max_iters=1000, val_interval=1000) +val_cfg = dict(type='ValLoop') +test_cfg = dict(type='TestLoop') +default_hooks = dict( + timer=dict(type='IterTimerHook'), + logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), + param_scheduler=dict(type='ParamSchedulerHook'), + checkpoint=dict( + type='CheckpointHook', by_epoch=False, interval=1000, + save_best='mIoU'), + sampler_seed=dict(type='DistSamplerSeedHook'), + visualization=dict(type='SegVisualizationHook')) diff --git a/configs/maskformer/maskformer_r50-d32_8xb2-20k_HOTS_v1-512x512.py b/configs/maskformer/maskformer_r50-d32_8xb2-20k_HOTS_v1-512x512.py new file mode 100644 index 0000000000..92b069208c --- /dev/null +++ b/configs/maskformer/maskformer_r50-d32_8xb2-20k_HOTS_v1-512x512.py @@ -0,0 +1,164 @@ +_base_ = [ + '../_base_/datasets/hots_v1_640x480.py', '../_base_/default_runtime.py' +] +norm_cfg = dict(type='SyncBN', requires_grad=True) +crop_size = (512, 512) +data_preprocessor = dict( + type='SegDataPreProcessor', + size=crop_size, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +# model_cfg +num_classes = 46 +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 1, 1), + strides=(1, 2, 2, 2), + norm_cfg=norm_cfg, + norm_eval=True, + style='pytorch', + contract_dilation=True, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + decode_head=dict( + type='MaskFormerHead', + in_channels=[256, 512, 1024, + 2048], # input channels of pixel_decoder modules + feat_channels=256, + in_index=[0, 1, 2, 3], + num_classes=num_classes, + out_channels=256, + num_queries=100, + pixel_decoder=dict( + type='mmdet.PixelDecoder', + norm_cfg=dict(type='GN', num_groups=32), + act_cfg=dict(type='ReLU')), + enforce_decoder_input_project=False, + positional_encoding=dict( # SinePositionalEncoding + num_feats=128, normalize=True), + transformer_decoder=dict( # DetrTransformerDecoder + return_intermediate=True, + num_layers=6, + layer_cfg=dict( # DetrTransformerDecoderLayer + self_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.1, + proj_drop=0.1, + dropout_layer=None, + batch_first=True), + cross_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.1, + proj_drop=0.1, + dropout_layer=None, + batch_first=True), + ffn_cfg=dict( + embed_dims=256, + feedforward_channels=2048, + num_fcs=2, + act_cfg=dict(type='ReLU', inplace=True), + ffn_drop=0.1, + dropout_layer=None, + add_identity=True)), + init_cfg=None), + loss_cls=dict( + type='mmdet.CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0, + reduction='mean', + class_weight=[1.0] * num_classes + [0.1]), + loss_mask=dict( + type='mmdet.FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + reduction='mean', + loss_weight=20.0), + loss_dice=dict( + type='mmdet.DiceLoss', + use_sigmoid=True, + activate=True, + reduction='mean', + naive_dice=True, + eps=1.0, + loss_weight=1.0), + train_cfg=dict( + assigner=dict( + type='mmdet.HungarianAssigner', + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + type='mmdet.FocalLossCost', + weight=20.0, + binary_input=True), + dict( + type='mmdet.DiceCost', + weight=1.0, + pred_act=True, + eps=1.0) + ]), + sampler=dict(type='mmdet.MaskPseudoSampler'))), + # training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole'), +) + +# dataset config +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=True), + dict( + type='RandomChoiceResize', + scales=[int(512 * x * 0.1) for x in range(5, 21)], + resize_type='ResizeShortestEdge', + max_size=2048), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs') +] +train_dataloader = dict(batch_size=2, dataset=dict(pipeline=train_pipeline)) + +# optimizer +optimizer = dict( + type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.0001) +optim_wrapper = dict( + type='OptimWrapper', + optimizer=optimizer, + clip_grad=dict(max_norm=0.01, norm_type=2), + paramwise_cfg=dict(custom_keys={ + 'backbone': dict(lr_mult=0.1), + })) +# learning policy +param_scheduler = [ + dict( + type='PolyLR', + eta_min=0, + power=0.9, + begin=0, + end=20000, + by_epoch=False) +] +train_cfg = dict( + type='IterBasedTrainLoop', max_iters=20000, val_interval=2000) +val_cfg = dict(type='ValLoop') +test_cfg = dict(type='TestLoop') +default_hooks = dict( + timer=dict(type='IterTimerHook'), + logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), + param_scheduler=dict(type='ParamSchedulerHook'), + checkpoint=dict( + type='CheckpointHook', by_epoch=False, interval=2000, + save_best='mIoU'), + sampler_seed=dict(type='DistSamplerSeedHook'), + visualization=dict(type='SegVisualizationHook')) diff --git a/configs/maskformer/maskformer_swin-s_upernet_8xb2-20k_HOTS_v1-512x512.py b/configs/maskformer/maskformer_swin-s_upernet_8xb2-20k_HOTS_v1-512x512.py new file mode 100644 index 0000000000..e4f0e35cde --- /dev/null +++ b/configs/maskformer/maskformer_swin-s_upernet_8xb2-20k_HOTS_v1-512x512.py @@ -0,0 +1,79 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth' # noqa +_base_ = './maskformer_r50-d32_8xb2-20k_HOTS_v1-512x512.py' +backbone_norm_cfg = dict(type='LN', requires_grad=True) +depths = [2, 2, 18, 2] +model = dict( + backbone=dict( + _delete_=True, + type='SwinTransformer', + pretrain_img_size=224, + embed_dims=96, + patch_size=4, + window_size=7, + mlp_ratio=4, + depths=depths, + num_heads=[3, 6, 12, 24], + strides=(4, 2, 2, 2), + out_indices=(0, 1, 2, 3), + qkv_bias=True, + qk_scale=None, + patch_norm=True, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.3, + use_abs_pos_embed=False, + act_cfg=dict(type='GELU'), + norm_cfg=backbone_norm_cfg, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file)), + decode_head=dict( + type='MaskFormerHead', + in_channels=[96, 192, 384, + 768], # input channels of pixel_decoder modules + )) + +# optimizer +optimizer = dict( + type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01) +# set all layers in backbone to lr_mult=1.0 +# set all norm layers, position_embeding, +# query_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=1.0, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +embed_multi = dict(decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=optimizer, + clip_grad=dict(max_norm=0.01, norm_type=2), + paramwise_cfg=dict(custom_keys=custom_keys)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + eta_min=0.0, + power=1.0, + begin=1500, + end=20000, + by_epoch=False, + ) +] diff --git a/configs/maskformer/maskformer_swin-t_upernet_8xb2-20k_HOTS_v1-512x512.py b/configs/maskformer/maskformer_swin-t_upernet_8xb2-20k_HOTS_v1-512x512.py new file mode 100644 index 0000000000..5cc5fe1bd5 --- /dev/null +++ b/configs/maskformer/maskformer_swin-t_upernet_8xb2-20k_HOTS_v1-512x512.py @@ -0,0 +1,81 @@ +_base_ = './maskformer_r50-d32_8xb2-20k_HOTS_v1-512x512.py' + +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' # noqa +backbone_norm_cfg = dict(type='LN', requires_grad=True) +depths = [2, 2, 6, 2] +model = dict( + backbone=dict( + _delete_=True, + type='SwinTransformer', + pretrain_img_size=224, + embed_dims=96, + patch_size=4, + window_size=7, + mlp_ratio=4, + depths=depths, + num_heads=[3, 6, 12, 24], + strides=(4, 2, 2, 2), + out_indices=(0, 1, 2, 3), + qkv_bias=True, + qk_scale=None, + patch_norm=True, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.3, + use_abs_pos_embed=False, + act_cfg=dict(type='GELU'), + norm_cfg=backbone_norm_cfg, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file)), + decode_head=dict( + type='MaskFormerHead', + in_channels=[96, 192, 384, + 768], # input channels of pixel_decoder modules + )) + +# optimizer +optimizer = dict( + type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01) + +# set all layers in backbone to lr_mult=1.0 +# set all norm layers, position_embeding, +# query_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=1.0, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +embed_multi = dict(decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=optimizer, + clip_grad=dict(max_norm=0.01, norm_type=2), + paramwise_cfg=dict(custom_keys=custom_keys)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + eta_min=0.0, + power=1.0, + begin=1500, + end=20000, + by_epoch=False, + ) +] diff --git a/configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb4-20k_HOTS_v1-640x480.py b/configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb4-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..ee25e43dc4 --- /dev/null +++ b/configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb4-20k_HOTS_v1-640x480.py @@ -0,0 +1,27 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +num_classes = 46 +model = dict( + data_preprocessor=data_preprocessor, + pretrained='mmcls://mobilenet_v2', + backbone=dict( + _delete_=True, + type='MobileNetV2', + widen_factor=1., + strides=(1, 2, 2, 1, 1, 1, 1), + dilations=(1, 1, 1, 2, 2, 4, 4), + out_indices=(1, 2, 4, 6), + norm_cfg=dict(type='SyncBN', requires_grad=True)), + decode_head=dict( + in_channels=320, + num_classes=num_classes + ), + auxiliary_head=dict( + in_channels=96, + num_classes=num_classes + ) +) diff --git a/configs/my_configs copy/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py b/configs/my_configs copy/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py new file mode 100644 index 0000000000..3a14da3ba1 --- /dev/null +++ b/configs/my_configs copy/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py @@ -0,0 +1,10 @@ +_base_ = '../bisenetv1/bisenetv1_r18-d32_4xb4-160k_coco-stuff164k-512x512.py' +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict( + backbone_cfg=dict( + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnet18_v1c'))), +) diff --git a/configs/my_configs copy/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py b/configs/my_configs copy/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py new file mode 100644 index 0000000000..5cea3371e9 --- /dev/null +++ b/configs/my_configs copy/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py @@ -0,0 +1,7 @@ +_base_ = '../bisenetv1/bisenetv1_r50-d32_4xb4-160k_coco-stuff164k-512x512.py' + +model = dict( + backbone=dict( + backbone_cfg=dict( + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnet50_v1c')))) diff --git a/configs/my_configs copy/ccnet_r101-d8_4xb4-40k_voc12aug-512x512.py b/configs/my_configs copy/ccnet_r101-d8_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..7963748d11 --- /dev/null +++ b/configs/my_configs copy/ccnet_r101-d8_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = '../ccnet/ccnet_r50-d8_4xb4-40k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/my_configs copy/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024.py b/configs/my_configs copy/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024.py new file mode 100644 index 0000000000..65b0ead547 --- /dev/null +++ b/configs/my_configs copy/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024.py @@ -0,0 +1,93 @@ +_base_ = [ + '../_base_/datasets/cityscapes_1024x1024.py', + '../_base_/default_runtime.py', +] + +# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa +# Licensed under the MIT License +class_weight = [ + 0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786, + 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529, + 1.0507 +] +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/pretrain/ddrnet23s-in1kpre_3rdparty-1ccac5b1.pth' # noqa +crop_size = (1024, 1024) +data_preprocessor = dict( + type='SegDataPreProcessor', + size=crop_size, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='DDRNet', + in_channels=3, + channels=32, + ppm_channels=128, + norm_cfg=norm_cfg, + align_corners=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), + decode_head=dict( + type='DDRHead', + in_channels=32 * 4, + channels=64, + dropout_ratio=0., + num_classes=19, + align_corners=False, + norm_cfg=norm_cfg, + loss_decode=[ + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + class_weight=class_weight, + loss_weight=1.0), + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + class_weight=class_weight, + loss_weight=0.4), + ]), + + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) + +train_dataloader = dict(batch_size=6, num_workers=4) + +iters = 120000 +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer, clip_grad=None) +# learning policy +param_scheduler = [ + dict( + type='PolyLR', + eta_min=0, + power=0.9, + begin=0, + end=iters, + by_epoch=False) +] + +# training schedule for 120k +train_cfg = dict( + type='IterBasedTrainLoop', max_iters=iters, val_interval=iters // 10) +val_cfg = dict(type='ValLoop') +test_cfg = dict(type='TestLoop') +default_hooks = dict( + timer=dict(type='IterTimerHook'), + logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), + param_scheduler=dict(type='ParamSchedulerHook'), + checkpoint=dict( + type='CheckpointHook', by_epoch=False, interval=iters // 10), + sampler_seed=dict(type='DistSamplerSeedHook'), + visualization=dict(type='SegVisualizationHook')) + +randomness = dict(seed=304) diff --git a/configs/my_configs copy/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024.py b/configs/my_configs copy/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024.py new file mode 100644 index 0000000000..444efe2b88 --- /dev/null +++ b/configs/my_configs copy/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024.py @@ -0,0 +1,93 @@ +_base_ = [ + '../_base_/datasets/cityscapes_1024x1024.py', + '../_base_/default_runtime.py', +] + +# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa +# Licensed under the MIT License +class_weight = [ + 0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786, + 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529, + 1.0507 +] +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/pretrain/ddrnet23-in1kpre_3rdparty-9ca29f62.pth' # noqa +crop_size = (1024, 1024) +data_preprocessor = dict( + type='SegDataPreProcessor', + size=crop_size, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='DDRNet', + in_channels=3, + channels=64, + ppm_channels=128, + norm_cfg=norm_cfg, + align_corners=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), + decode_head=dict( + type='DDRHead', + in_channels=64 * 4, + channels=128, + dropout_ratio=0., + num_classes=19, + align_corners=False, + norm_cfg=norm_cfg, + loss_decode=[ + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + class_weight=class_weight, + loss_weight=1.0), + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + class_weight=class_weight, + loss_weight=0.4), + ]), + + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) + +train_dataloader = dict(batch_size=6, num_workers=4) + +iters = 120000 +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer, clip_grad=None) +# learning policy +param_scheduler = [ + dict( + type='PolyLR', + eta_min=0, + power=0.9, + begin=0, + end=iters, + by_epoch=False) +] + +# training schedule for 120k +train_cfg = dict( + type='IterBasedTrainLoop', max_iters=iters, val_interval=iters // 10) +val_cfg = dict(type='ValLoop') +test_cfg = dict(type='TestLoop') +default_hooks = dict( + timer=dict(type='IterTimerHook'), + logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), + param_scheduler=dict(type='ParamSchedulerHook'), + checkpoint=dict( + type='CheckpointHook', by_epoch=False, interval=iters // 10), + sampler_seed=dict(type='DistSamplerSeedHook'), + visualization=dict(type='SegVisualizationHook')) + +randomness = dict(seed=304) diff --git a/configs/my_configs copy/deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512.py b/configs/my_configs copy/deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..0659669c90 --- /dev/null +++ b/configs/my_configs copy/deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = '../deeplabv3/deeplabv3_r50-d8_4xb4-20k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/my_configs copy/deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480.py b/configs/my_configs copy/deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480.py new file mode 100644 index 0000000000..5a882e16e3 --- /dev/null +++ b/configs/my_configs copy/deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480.py @@ -0,0 +1,2 @@ +_base_ = '../deeplabv3/deeplabv3_r50-d8_4xb4-40k_pascal-context-59-480x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/my_configs copy/deeplabv3_r18b-d8_4xb2-80k_cityscapes-512x1024.py b/configs/my_configs copy/deeplabv3_r18b-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..7ad6387591 --- /dev/null +++ b/configs/my_configs copy/deeplabv3_r18b-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,9 @@ +_base_ = '../deeplabv3/deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(type='ResNet', depth=18), + decode_head=dict( + in_channels=512, + channels=128, + ), + auxiliary_head=dict(in_channels=256, channels=64)) diff --git a/configs/my_configs copy/deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py b/configs/my_configs copy/deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py new file mode 100644 index 0000000000..c3b4f94f8c --- /dev/null +++ b/configs/my_configs copy/deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py @@ -0,0 +1,11 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', + '../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_320k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=171), + auxiliary_head=dict(num_classes=171)) diff --git a/configs/my_configs copy/deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512.py b/configs/my_configs copy/deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512.py new file mode 100644 index 0000000000..40dbffad45 --- /dev/null +++ b/configs/my_configs copy/deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512.py @@ -0,0 +1,11 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', + '../_base_/datasets/coco-stuff10k.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_40k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=171), + auxiliary_head=dict(num_classes=171)) diff --git a/configs/my_configs copy/fast_scnn_8xb4-160k_cityscapes-512x1024.py b/configs/my_configs copy/fast_scnn_8xb4-160k_cityscapes-512x1024.py new file mode 100644 index 0000000000..e7f68bfe73 --- /dev/null +++ b/configs/my_configs copy/fast_scnn_8xb4-160k_cityscapes-512x1024.py @@ -0,0 +1,15 @@ +_base_ = [ + '../_base_/models/fast_scnn.py', '../_base_/datasets/cityscapes.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (512, 1024) +data_preprocessor = dict(size=crop_size) +model = dict(data_preprocessor=data_preprocessor) +# Re-config the data sampler. +train_dataloader = dict(batch_size=4, num_workers=4) +val_dataloader = dict(batch_size=1, num_workers=4) +test_dataloader = val_dataloader + +# Re-config the optimizer. +optimizer = dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=4e-5) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer) diff --git a/configs/my_configs copy/fcn_hr18_4xb2-160k_cityscapes-512x1024.py b/configs/my_configs copy/fcn_hr18_4xb2-160k_cityscapes-512x1024.py new file mode 100644 index 0000000000..0b374632b8 --- /dev/null +++ b/configs/my_configs copy/fcn_hr18_4xb2-160k_cityscapes-512x1024.py @@ -0,0 +1,7 @@ +_base_ = [ + '../_base_/models/fcn_hr18.py', '../_base_/datasets/cityscapes.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (512, 1024) +data_preprocessor = dict(size=crop_size) +model = dict(data_preprocessor=data_preprocessor) diff --git a/configs/my_configs copy/fcn_hr18_4xb4-40k_voc12aug-512x512.py b/configs/my_configs copy/fcn_hr18_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..2aa16b124d --- /dev/null +++ b/configs/my_configs copy/fcn_hr18_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,8 @@ +_base_ = [ + '../_base_/models/fcn_hr18.py', '../_base_/datasets/pascal_voc12_aug.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, decode_head=dict(num_classes=21)) diff --git a/configs/my_configs copy/fcn_hr18s_4xb2-160k_cityscapes-512x1024.py b/configs/my_configs copy/fcn_hr18s_4xb2-160k_cityscapes-512x1024.py new file mode 100644 index 0000000000..02915e3d55 --- /dev/null +++ b/configs/my_configs copy/fcn_hr18s_4xb2-160k_cityscapes-512x1024.py @@ -0,0 +1,9 @@ +_base_ = '../hrnet/fcn_hr18_4xb2-160k_cityscapes-512x1024.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/my_configs copy/fcn_hr18s_4xb4-40k_voc12aug-512x512.py b/configs/my_configs copy/fcn_hr18s_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..73bf914a2a --- /dev/null +++ b/configs/my_configs copy/fcn_hr18s_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,9 @@ +_base_ = '../hrnet/fcn_hr18_4xb4-40k_voc12aug-512x512.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/my_configs copy/fcn_hr48_4xb4-80k_pascal-context-59-480x480.py b/configs/my_configs copy/fcn_hr48_4xb4-80k_pascal-context-59-480x480.py new file mode 100644 index 0000000000..4ac031ac42 --- /dev/null +++ b/configs/my_configs copy/fcn_hr48_4xb4-80k_pascal-context-59-480x480.py @@ -0,0 +1,10 @@ +_base_ = '../hrnet/fcn_hr18_4xb4-80k_pascal-context-59-480x480.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w48', + backbone=dict( + extra=dict( + stage2=dict(num_channels=(48, 96)), + stage3=dict(num_channels=(48, 96, 192)), + stage4=dict(num_channels=(48, 96, 192, 384)))), + decode_head=dict( + in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384]))) diff --git a/configs/my_configs copy/fcn_r50-d8_4xb4-20k_voc12aug-512x512.py b/configs/my_configs copy/fcn_r50-d8_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..eac86d5389 --- /dev/null +++ b/configs/my_configs copy/fcn_r50-d8_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,10 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/pascal_voc12_aug.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=21), + auxiliary_head=dict(num_classes=21)) diff --git a/configs/my_configs copy/fpn_r50_4xb4-160k_ade20k-512x512.py b/configs/my_configs copy/fpn_r50_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..4e4bc57788 --- /dev/null +++ b/configs/my_configs copy/fpn_r50_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,8 @@ +_base_ = [ + '../_base_/models/fpn_r50.py', '../_base_/datasets/ade20k.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, decode_head=dict(num_classes=150)) diff --git a/configs/my_configs copy/gcnet_r50-d8_4xb4-20k_voc12aug-512x512.py b/configs/my_configs copy/gcnet_r50-d8_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..356b088236 --- /dev/null +++ b/configs/my_configs copy/gcnet_r50-d8_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,11 @@ +_base_ = [ + '../_base_/models/gcnet_r50-d8.py', + '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_20k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=21), + auxiliary_head=dict(num_classes=21)) diff --git a/configs/my_configs copy/icnet_r18-d8_4xb2-160k_cityscapes-832x832.py b/configs/my_configs copy/icnet_r18-d8_4xb2-160k_cityscapes-832x832.py new file mode 100644 index 0000000000..8765b9bf78 --- /dev/null +++ b/configs/my_configs copy/icnet_r18-d8_4xb2-160k_cityscapes-832x832.py @@ -0,0 +1,3 @@ +_base_ = '../icnet/icnet_r50-d8_4xb2-160k_cityscapes-832x832.py' +model = dict( + backbone=dict(layer_channels=(128, 512), backbone_cfg=dict(depth=18))) diff --git a/configs/my_configs copy/isanet_r101-d8_4xb4-20k_voc12aug-512x512.py b/configs/my_configs copy/isanet_r101-d8_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..1010b19f61 --- /dev/null +++ b/configs/my_configs copy/isanet_r101-d8_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = '../isanet/isanet_r50-d8_4xb4-20k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/my_configs copy/isanet_r50-d8_4xb4-20k_voc12aug-512x512.py b/configs/my_configs copy/isanet_r50-d8_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..591df42a87 --- /dev/null +++ b/configs/my_configs copy/isanet_r50-d8_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,11 @@ +_base_ = [ + '../_base_/models/isanet_r50-d8.py', + '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_20k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=21), + auxiliary_head=dict(num_classes=21)) diff --git a/configs/my_configs copy/mask2former_r101_8xb2-160k_ade20k-512x512.py b/configs/my_configs copy/mask2former_r101_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..6bec6d5fe4 --- /dev/null +++ b/configs/my_configs copy/mask2former_r101_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,7 @@ +_base_ = ['../mask2former/mask2former_r50_8xb2-160k_ade20k-512x512.py'] + +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/my_configs copy/mask2former_r50_8xb2-160k_ade20k-512x512.py b/configs/my_configs copy/mask2former_r50_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..78cf60510c --- /dev/null +++ b/configs/my_configs copy/mask2former_r50_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,200 @@ +_base_ = ['../_base_/default_runtime.py', '../_base_/datasets/ade20k.py'] + +custom_imports = dict(imports='mmdet.models', allow_failed_imports=False) + +crop_size = (512, 512) +data_preprocessor = dict( + type='SegDataPreProcessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255, + size=crop_size, + test_cfg=dict(size_divisor=32)) +num_classes = 150 +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='ResNet', + depth=50, + deep_stem=False, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=-1, + norm_cfg=dict(type='SyncBN', requires_grad=False), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + decode_head=dict( + type='Mask2FormerHead', + in_channels=[256, 512, 1024, 2048], + strides=[4, 8, 16, 32], + feat_channels=256, + out_channels=256, + num_classes=num_classes, + num_queries=100, + num_transformer_feat_level=3, + align_corners=False, + pixel_decoder=dict( + type='mmdet.MSDeformAttnPixelDecoder', + num_outs=3, + norm_cfg=dict(type='GN', num_groups=32), + act_cfg=dict(type='ReLU'), + encoder=dict( # DeformableDetrTransformerEncoder + num_layers=6, + layer_cfg=dict( # DeformableDetrTransformerEncoderLayer + self_attn_cfg=dict( # MultiScaleDeformableAttention + embed_dims=256, + num_heads=8, + num_levels=3, + num_points=4, + im2col_step=64, + dropout=0.0, + batch_first=True, + norm_cfg=None, + init_cfg=None), + ffn_cfg=dict( + embed_dims=256, + feedforward_channels=1024, + num_fcs=2, + ffn_drop=0.0, + act_cfg=dict(type='ReLU', inplace=True))), + init_cfg=None), + positional_encoding=dict( # SinePositionalEncoding + num_feats=128, normalize=True), + init_cfg=None), + enforce_decoder_input_project=False, + positional_encoding=dict( # SinePositionalEncoding + num_feats=128, normalize=True), + transformer_decoder=dict( # Mask2FormerTransformerDecoder + return_intermediate=True, + num_layers=9, + layer_cfg=dict( # Mask2FormerTransformerDecoderLayer + self_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.0, + proj_drop=0.0, + dropout_layer=None, + batch_first=True), + cross_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.0, + proj_drop=0.0, + dropout_layer=None, + batch_first=True), + ffn_cfg=dict( + embed_dims=256, + feedforward_channels=2048, + num_fcs=2, + act_cfg=dict(type='ReLU', inplace=True), + ffn_drop=0.0, + dropout_layer=None, + add_identity=True)), + init_cfg=None), + loss_cls=dict( + type='mmdet.CrossEntropyLoss', + use_sigmoid=False, + loss_weight=2.0, + reduction='mean', + class_weight=[1.0] * num_classes + [0.1]), + loss_mask=dict( + type='mmdet.CrossEntropyLoss', + use_sigmoid=True, + reduction='mean', + loss_weight=5.0), + loss_dice=dict( + type='mmdet.DiceLoss', + use_sigmoid=True, + activate=True, + reduction='mean', + naive_dice=True, + eps=1.0, + loss_weight=5.0), + train_cfg=dict( + num_points=12544, + oversample_ratio=3.0, + importance_sample_ratio=0.75, + assigner=dict( + type='mmdet.HungarianAssigner', + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + weight=5.0, + use_sigmoid=True), + dict( + type='mmdet.DiceCost', + weight=5.0, + pred_act=True, + eps=1.0) + ]), + sampler=dict(type='mmdet.MaskPseudoSampler'))), + train_cfg=dict(), + test_cfg=dict(mode='whole')) + +# dataset config +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=True), + dict( + type='RandomChoiceResize', + scales=[int(512 * x * 0.1) for x in range(5, 21)], + resize_type='ResizeShortestEdge', + max_size=2048), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs') +] +train_dataloader = dict(batch_size=2, dataset=dict(pipeline=train_pipeline)) + +# optimizer +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +optimizer = dict( + type='AdamW', lr=0.0001, weight_decay=0.05, eps=1e-8, betas=(0.9, 0.999)) +optim_wrapper = dict( + type='OptimWrapper', + optimizer=optimizer, + clip_grad=dict(max_norm=0.01, norm_type=2), + paramwise_cfg=dict( + custom_keys={ + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi, + }, + norm_decay_mult=0.0)) +# learning policy +param_scheduler = [ + dict( + type='PolyLR', + eta_min=0, + power=0.9, + begin=0, + end=160000, + by_epoch=False) +] + +# training schedule for 160k +train_cfg = dict( + type='IterBasedTrainLoop', max_iters=160000, val_interval=5000) +val_cfg = dict(type='ValLoop') +test_cfg = dict(type='TestLoop') +default_hooks = dict( + timer=dict(type='IterTimerHook'), + logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), + param_scheduler=dict(type='ParamSchedulerHook'), + checkpoint=dict( + type='CheckpointHook', by_epoch=False, interval=5000, + save_best='mIoU'), + sampler_seed=dict(type='DistSamplerSeedHook'), + visualization=dict(type='SegVisualizationHook')) + +# Default setting for scaling LR automatically +# - `enable` means enable scaling LR automatically +# or not by default. +# - `base_batch_size` = (8 GPUs) x (2 samples per GPU). +auto_scale_lr = dict(enable=False, base_batch_size=16) diff --git a/configs/my_configs copy/mask2former_swin-s_8xb2-160k_ade20k-512x512.py b/configs/my_configs copy/mask2former_swin-s_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..78c4dfabb9 --- /dev/null +++ b/configs/my_configs copy/mask2former_swin-s_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,37 @@ +_base_ = ['../mask2former/mask2former_swin-t_8xb2-160k_ade20k-512x512.py'] +pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth' # noqa + +depths = [2, 2, 18, 2] +model = dict( + backbone=dict( + depths=depths, init_cfg=dict(type='Pretrained', + checkpoint=pretrained))) + +# set all layers in backbone to lr_mult=0.1 +# set all norm layers, position_embeding, +# query_embeding, level_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0) +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'absolute_pos_embed': backbone_embed_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optim_wrapper = dict( + paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0)) diff --git a/configs/my_configs copy/mask2former_swin-t_8xb2-160k_ade20k-512x512.py b/configs/my_configs copy/mask2former_swin-t_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..f08d9d61a4 --- /dev/null +++ b/configs/my_configs copy/mask2former_swin-t_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,52 @@ +_base_ = ['../mask2former/mask2former_r50_8xb2-160k_ade20k-512x512.py'] +pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' # noqa +depths = [2, 2, 6, 2] +model = dict( + backbone=dict( + _delete_=True, + type='SwinTransformer', + embed_dims=96, + depths=depths, + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.3, + patch_norm=True, + out_indices=(0, 1, 2, 3), + with_cp=False, + frozen_stages=-1, + init_cfg=dict(type='Pretrained', checkpoint=pretrained)), + decode_head=dict(in_channels=[96, 192, 384, 768])) + +# set all layers in backbone to lr_mult=0.1 +# set all norm layers, position_embeding, +# query_embeding, level_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0) +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'absolute_pos_embed': backbone_embed_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optim_wrapper = dict( + paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0)) diff --git a/configs/my_configs copy/maskformer_r101-d32_8xb2-160k_ade20k-512x512.py b/configs/my_configs copy/maskformer_r101-d32_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..a270436104 --- /dev/null +++ b/configs/my_configs copy/maskformer_r101-d32_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,7 @@ +_base_ = '../maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512.py' + +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/my_configs copy/maskformer_r50-d32_8xb2-160k_ade20k-512x512.py b/configs/my_configs copy/maskformer_r50-d32_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..2a83746171 --- /dev/null +++ b/configs/my_configs copy/maskformer_r50-d32_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,141 @@ +_base_ = [ + '../_base_/datasets/ade20k.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_160k.py' +] +norm_cfg = dict(type='SyncBN', requires_grad=True) +crop_size = (512, 512) +data_preprocessor = dict( + type='SegDataPreProcessor', + size=crop_size, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +# model_cfg +num_classes = 150 +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 1, 1), + strides=(1, 2, 2, 2), + norm_cfg=norm_cfg, + norm_eval=True, + style='pytorch', + contract_dilation=True, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + decode_head=dict( + type='MaskFormerHead', + in_channels=[256, 512, 1024, + 2048], # input channels of pixel_decoder modules + feat_channels=256, + in_index=[0, 1, 2, 3], + num_classes=150, + out_channels=256, + num_queries=100, + pixel_decoder=dict( + type='mmdet.PixelDecoder', + norm_cfg=dict(type='GN', num_groups=32), + act_cfg=dict(type='ReLU')), + enforce_decoder_input_project=False, + positional_encoding=dict( # SinePositionalEncoding + num_feats=128, normalize=True), + transformer_decoder=dict( # DetrTransformerDecoder + return_intermediate=True, + num_layers=6, + layer_cfg=dict( # DetrTransformerDecoderLayer + self_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.1, + proj_drop=0.1, + dropout_layer=None, + batch_first=True), + cross_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.1, + proj_drop=0.1, + dropout_layer=None, + batch_first=True), + ffn_cfg=dict( + embed_dims=256, + feedforward_channels=2048, + num_fcs=2, + act_cfg=dict(type='ReLU', inplace=True), + ffn_drop=0.1, + dropout_layer=None, + add_identity=True)), + init_cfg=None), + loss_cls=dict( + type='mmdet.CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0, + reduction='mean', + class_weight=[1.0] * num_classes + [0.1]), + loss_mask=dict( + type='mmdet.FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + reduction='mean', + loss_weight=20.0), + loss_dice=dict( + type='mmdet.DiceLoss', + use_sigmoid=True, + activate=True, + reduction='mean', + naive_dice=True, + eps=1.0, + loss_weight=1.0), + train_cfg=dict( + assigner=dict( + type='mmdet.HungarianAssigner', + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + type='mmdet.FocalLossCost', + weight=20.0, + binary_input=True), + dict( + type='mmdet.DiceCost', + weight=1.0, + pred_act=True, + eps=1.0) + ]), + sampler=dict(type='mmdet.MaskPseudoSampler'))), + # training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole'), +) +# optimizer +optimizer = dict( + type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.0001) +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=optimizer, + clip_grad=dict(max_norm=0.01, norm_type=2), + paramwise_cfg=dict(custom_keys={ + 'backbone': dict(lr_mult=0.1), + })) +# learning policy +param_scheduler = [ + dict( + type='PolyLR', + eta_min=0, + power=0.9, + begin=0, + end=160000, + by_epoch=False) +] + +# In MaskFormer implementation we use batch size 2 per GPU as default +train_dataloader = dict(batch_size=2, num_workers=2) +val_dataloader = dict(batch_size=1, num_workers=4) +test_dataloader = val_dataloader diff --git a/configs/my_configs copy/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py b/configs/my_configs copy/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..57d431c423 --- /dev/null +++ b/configs/my_configs copy/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,81 @@ +_base_ = '../maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512.py' + +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' # noqa +backbone_norm_cfg = dict(type='LN', requires_grad=True) +depths = [2, 2, 6, 2] +model = dict( + backbone=dict( + _delete_=True, + type='SwinTransformer', + pretrain_img_size=224, + embed_dims=96, + patch_size=4, + window_size=7, + mlp_ratio=4, + depths=depths, + num_heads=[3, 6, 12, 24], + strides=(4, 2, 2, 2), + out_indices=(0, 1, 2, 3), + qkv_bias=True, + qk_scale=None, + patch_norm=True, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.3, + use_abs_pos_embed=False, + act_cfg=dict(type='GELU'), + norm_cfg=backbone_norm_cfg, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file)), + decode_head=dict( + type='MaskFormerHead', + in_channels=[96, 192, 384, + 768], # input channels of pixel_decoder modules + )) + +# optimizer +optimizer = dict( + type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01) + +# set all layers in backbone to lr_mult=1.0 +# set all norm layers, position_embeding, +# query_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=1.0, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +embed_multi = dict(decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=optimizer, + clip_grad=dict(max_norm=0.01, norm_type=2), + paramwise_cfg=dict(custom_keys=custom_keys)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + eta_min=0.0, + power=1.0, + begin=1500, + end=160000, + by_epoch=False, + ) +] diff --git a/configs/my_configs copy/mobilenet-v2-d8_fcn_4xb4-160k_ade20k-512x512.py b/configs/my_configs copy/mobilenet-v2-d8_fcn_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..015fa6f201 --- /dev/null +++ b/configs/my_configs copy/mobilenet-v2-d8_fcn_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,13 @@ +_base_ = '../fcn/fcn_r101-d8_4xb4-160k_ade20k-512x512.py' +model = dict( + pretrained='mmcls://mobilenet_v2', + backbone=dict( + _delete_=True, + type='MobileNetV2', + widen_factor=1., + strides=(1, 2, 2, 1, 1, 1, 1), + dilations=(1, 1, 1, 2, 2, 4, 4), + out_indices=(1, 2, 4, 6), + norm_cfg=dict(type='SyncBN', requires_grad=True)), + decode_head=dict(in_channels=320), + auxiliary_head=dict(in_channels=96)) diff --git a/configs/my_configs copy/mobilenet-v2-d8_pspnet_4xb4-160k_ade20k-512x512.py b/configs/my_configs copy/mobilenet-v2-d8_pspnet_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..73db59beae --- /dev/null +++ b/configs/my_configs copy/mobilenet-v2-d8_pspnet_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,13 @@ +_base_ = '../pspnet/pspnet_r101-d8_4xb4-160k_ade20k-512x512.py' +model = dict( + pretrained='mmcls://mobilenet_v2', + backbone=dict( + _delete_=True, + type='MobileNetV2', + widen_factor=1., + strides=(1, 2, 2, 1, 1, 1, 1), + dilations=(1, 1, 1, 2, 2, 4, 4), + out_indices=(1, 2, 4, 6), + norm_cfg=dict(type='SyncBN', requires_grad=True)), + decode_head=dict(in_channels=320), + auxiliary_head=dict(in_channels=96)) diff --git a/configs/my_configs copy/nonlocal_r101-d8_4xb4-40k_voc12aug-512x512.py b/configs/my_configs copy/nonlocal_r101-d8_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..8bc7045ef4 --- /dev/null +++ b/configs/my_configs copy/nonlocal_r101-d8_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = '../nonlocal_net/nonlocal_r50-d8_4xb4-40k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/my_configs copy/ocrnet_hr18_4xb4-40k_voc12aug-512x512.py b/configs/my_configs copy/ocrnet_hr18_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..c6b69ea632 --- /dev/null +++ b/configs/my_configs copy/ocrnet_hr18_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,40 @@ +_base_ = [ + '../_base_/models/ocrnet_hr18.py', + '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_40k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=[ + dict( + type='FCNHead', + in_channels=[18, 36, 72, 144], + channels=sum([18, 36, 72, 144]), + in_index=(0, 1, 2, 3), + input_transform='resize_concat', + kernel_size=1, + num_convs=1, + concat_input=False, + dropout_ratio=-1, + num_classes=21, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + dict( + type='OCRHead', + in_channels=[18, 36, 72, 144], + in_index=(0, 1, 2, 3), + input_transform='resize_concat', + channels=512, + ocr_channels=256, + dropout_ratio=-1, + num_classes=21, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + ]) diff --git a/configs/my_configs copy/ocrnet_hr18s_4xb4-40k_voc12aug-512x512.py b/configs/my_configs copy/ocrnet_hr18s_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..b49b9c3282 --- /dev/null +++ b/configs/my_configs copy/ocrnet_hr18s_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,9 @@ +_base_ = '../ocrnet/ocrnet_hr18_4xb4-40k_voc12aug-512x512.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/my_configs copy/pidnet-l_2xb6-120k_1024x1024-cityscapes.py b/configs/my_configs copy/pidnet-l_2xb6-120k_1024x1024-cityscapes.py new file mode 100644 index 0000000000..2d4073888d --- /dev/null +++ b/configs/my_configs copy/pidnet-l_2xb6-120k_1024x1024-cityscapes.py @@ -0,0 +1,10 @@ +_base_ = '../pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes.py' +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/pidnet/pidnet-l_imagenet1k_20230306-67889109.pth' # noqa +model = dict( + backbone=dict( + channels=64, + ppm_channels=112, + num_stem_blocks=3, + num_branch_blocks=4, + init_cfg=dict(checkpoint=checkpoint_file)), + decode_head=dict(in_channels=256, channels=256)) diff --git a/configs/my_configs copy/pidnet-m_2xb6-120k_1024x1024-cityscapes.py b/configs/my_configs copy/pidnet-m_2xb6-120k_1024x1024-cityscapes.py new file mode 100644 index 0000000000..cd881f3f5d --- /dev/null +++ b/configs/my_configs copy/pidnet-m_2xb6-120k_1024x1024-cityscapes.py @@ -0,0 +1,5 @@ +_base_ = '../pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes.py' +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/pidnet/pidnet-m_imagenet1k_20230306-39893c52.pth' # noqa +model = dict( + backbone=dict(channels=64, init_cfg=dict(checkpoint=checkpoint_file)), + decode_head=dict(in_channels=256)) diff --git a/configs/my_configs copy/pidnet-s_2xb6-120k_1024x1024-cityscapes.py b/configs/my_configs copy/pidnet-s_2xb6-120k_1024x1024-cityscapes.py new file mode 100644 index 0000000000..f70ca4287a --- /dev/null +++ b/configs/my_configs copy/pidnet-s_2xb6-120k_1024x1024-cityscapes.py @@ -0,0 +1,113 @@ +_base_ = [ + '../_base_/datasets/cityscapes_1024x1024.py', + '../_base_/default_runtime.py' +] + +# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa +# Licensed under the MIT License +class_weight = [ + 0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786, + 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529, + 1.0507 +] +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/pidnet/pidnet-s_imagenet1k_20230306-715e6273.pth' # noqa +crop_size = (1024, 1024) +data_preprocessor = dict( + type='SegDataPreProcessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255, + size=crop_size) +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='PIDNet', + in_channels=3, + channels=32, + ppm_channels=96, + num_stem_blocks=2, + num_branch_blocks=3, + align_corners=False, + norm_cfg=norm_cfg, + act_cfg=dict(type='ReLU', inplace=True), + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file)), + decode_head=dict( + type='PIDHead', + in_channels=128, + channels=128, + num_classes=19, + norm_cfg=norm_cfg, + act_cfg=dict(type='ReLU', inplace=True), + align_corners=True, + loss_decode=[ + dict( + type='CrossEntropyLoss', + use_sigmoid=False, + class_weight=class_weight, + loss_weight=0.4), + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + class_weight=class_weight, + loss_weight=1.0), + dict(type='BoundaryLoss', loss_weight=20.0), + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + class_weight=class_weight, + loss_weight=1.0) + ]), + train_cfg=dict(), + test_cfg=dict(mode='whole')) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + type='RandomResize', + scale=(2048, 1024), + ratio_range=(0.5, 2.0), + keep_ratio=True), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='GenerateEdge', edge_width=4), + dict(type='PackSegInputs') +] +train_dataloader = dict(batch_size=6, dataset=dict(pipeline=train_pipeline)) + +iters = 120000 +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer, clip_grad=None) +# learning policy +param_scheduler = [ + dict( + type='PolyLR', + eta_min=0, + power=0.9, + begin=0, + end=iters, + by_epoch=False) +] +# training schedule for 120k +train_cfg = dict( + type='IterBasedTrainLoop', max_iters=iters, val_interval=iters // 10) +val_cfg = dict(type='ValLoop') +test_cfg = dict(type='TestLoop') +default_hooks = dict( + timer=dict(type='IterTimerHook'), + logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), + param_scheduler=dict(type='ParamSchedulerHook'), + checkpoint=dict( + type='CheckpointHook', by_epoch=False, interval=iters // 10), + sampler_seed=dict(type='DistSamplerSeedHook'), + visualization=dict(type='SegVisualizationHook')) + +randomness = dict(seed=304) diff --git a/configs/my_configs copy/pspnet_r101-d8_4xb4-40k_voc12aug-512x512.py b/configs/my_configs copy/pspnet_r101-d8_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..43568a7799 --- /dev/null +++ b/configs/my_configs copy/pspnet_r101-d8_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = '../pspnet/pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/my_configs copy/pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480.py b/configs/my_configs copy/pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480.py new file mode 100644 index 0000000000..eed77201d5 --- /dev/null +++ b/configs/my_configs copy/pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480.py @@ -0,0 +1,2 @@ +_base_ = '../pspnet/pspnet_r50-d8_4xb4-80k_pascal-context-59-480x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/my_configs copy/pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024.py b/configs/my_configs copy/pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..1f83edeaa2 --- /dev/null +++ b/configs/my_configs copy/pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,9 @@ +_base_ = '../pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(type='ResNet', depth=18), + decode_head=dict( + in_channels=512, + channels=128, + ), + auxiliary_head=dict(in_channels=256, channels=64)) diff --git a/configs/my_configs copy/pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py b/configs/my_configs copy/pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py new file mode 100644 index 0000000000..2a9ce4c4f1 --- /dev/null +++ b/configs/my_configs copy/pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py @@ -0,0 +1,11 @@ +_base_ = [ + '../_base_/models/pspnet_r50-d8.py', + '../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_320k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=171), + auxiliary_head=dict(num_classes=171)) diff --git a/configs/my_configs copy/pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512.py b/configs/my_configs copy/pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512.py new file mode 100644 index 0000000000..fae57b0dbc --- /dev/null +++ b/configs/my_configs copy/pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512.py @@ -0,0 +1,10 @@ +_base_ = [ + '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/coco-stuff10k.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=171), + auxiliary_head=dict(num_classes=171)) diff --git a/configs/my_configs copy/pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py b/configs/my_configs copy/pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..c4a4611530 --- /dev/null +++ b/configs/my_configs copy/pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,11 @@ +_base_ = [ + '../_base_/models/pspnet_r50-d8.py', + '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_40k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=21), + auxiliary_head=dict(num_classes=21)) diff --git a/configs/my_configs copy/segformer_mit-b0_8xb2-160k_ade20k-512x512.py b/configs/my_configs copy/segformer_mit-b0_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..4a9476df68 --- /dev/null +++ b/configs/my_configs copy/segformer_mit-b0_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/segformer_mit-b0.py', '../_base_/datasets/ade20k.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' # noqa +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict(init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), + decode_head=dict(num_classes=150)) + +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict( + type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01), + paramwise_cfg=dict( + custom_keys={ + 'pos_block': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.), + 'head': dict(lr_mult=10.) + })) + +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + eta_min=0.0, + power=1.0, + begin=1500, + end=160000, + by_epoch=False, + ) +] +train_dataloader = dict(batch_size=2, num_workers=2) +val_dataloader = dict(batch_size=1, num_workers=4) +test_dataloader = val_dataloader diff --git a/configs/my_configs copy/segmenter_vit-s_fcn_8xb1-160k_ade20k-512x512.py b/configs/my_configs copy/segmenter_vit-s_fcn_8xb1-160k_ade20k-512x512.py new file mode 100644 index 0000000000..4e7ef59aa0 --- /dev/null +++ b/configs/my_configs copy/segmenter_vit-s_fcn_8xb1-160k_ade20k-512x512.py @@ -0,0 +1,14 @@ +_base_ = '../segmenter/segmenter_vit-s_mask_8xb1-160k_ade20k-512x512.py' + +model = dict( + decode_head=dict( + _delete_=True, + type='FCNHead', + in_channels=384, + channels=384, + num_convs=0, + dropout_ratio=0.0, + concat_input=False, + num_classes=150, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))) diff --git a/configs/my_configs copy/segmenter_vit-t_mask_8xb1-160k_ade20k-512x512.py b/configs/my_configs copy/segmenter_vit-t_mask_8xb1-160k_ade20k-512x512.py new file mode 100644 index 0000000000..221a9f9a6d --- /dev/null +++ b/configs/my_configs copy/segmenter_vit-t_mask_8xb1-160k_ade20k-512x512.py @@ -0,0 +1,26 @@ +_base_ = [ + '../_base_/models/segmenter_vit-b16_mask.py', + '../_base_/datasets/ade20k.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_160k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segmenter/vit_tiny_p16_384_20220308-cce8c795.pth' # noqa + +model = dict( + data_preprocessor=data_preprocessor, + pretrained=checkpoint, + backbone=dict(embed_dims=192, num_heads=3), + decode_head=dict( + type='SegmenterMaskTransformerHead', + in_channels=192, + channels=192, + num_heads=3, + embed_dims=192)) + +optimizer = dict(lr=0.001, weight_decay=0.0) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer) +train_dataloader = dict( + # num_gpus: 8 -> batch_size: 8 + batch_size=1) +val_dataloader = dict(batch_size=1) diff --git a/configs/my_configs copy/segnext_mscan-b_1xb16-adamw-160k_ade20k-512x512.py b/configs/my_configs copy/segnext_mscan-b_1xb16-adamw-160k_ade20k-512x512.py new file mode 100644 index 0000000000..59adb822d8 --- /dev/null +++ b/configs/my_configs copy/segnext_mscan-b_1xb16-adamw-160k_ade20k-512x512.py @@ -0,0 +1,28 @@ +_base_ = '../segnext/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py' + +# model settings +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_b_20230227-3ab7d230.pth' # noqa +ham_norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + type='EncoderDecoder', + backbone=dict( + embed_dims=[64, 128, 320, 512], + depths=[3, 3, 12, 3], + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file), + drop_path_rate=0.1, + norm_cfg=dict(type='BN', requires_grad=True)), + decode_head=dict( + type='LightHamHead', + in_channels=[128, 320, 512], + in_index=[1, 2, 3], + channels=512, + ham_channels=512, + dropout_ratio=0.1, + num_classes=150, + norm_cfg=ham_norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/configs/my_configs copy/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py b/configs/my_configs copy/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py new file mode 100644 index 0000000000..c8d6da85ff --- /dev/null +++ b/configs/my_configs copy/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py @@ -0,0 +1,84 @@ +_base_ = [ + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py', + '../_base_/datasets/ade20k.py' +] +# model settings +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' # noqa +ham_norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +crop_size = (512, 512) +data_preprocessor = dict( + type='SegDataPreProcessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255, + size=(512, 512), + test_cfg=dict(size_divisor=32)) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + pretrained=None, + backbone=dict( + type='MSCAN', + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file), + embed_dims=[32, 64, 160, 256], + mlp_ratios=[8, 8, 4, 4], + drop_rate=0.0, + drop_path_rate=0.1, + depths=[3, 3, 5, 2], + attention_kernel_sizes=[5, [1, 7], [1, 11], [1, 21]], + attention_kernel_paddings=[2, [0, 3], [0, 5], [0, 10]], + act_cfg=dict(type='GELU'), + norm_cfg=dict(type='BN', requires_grad=True)), + decode_head=dict( + type='LightHamHead', + in_channels=[64, 160, 256], + in_index=[1, 2, 3], + channels=256, + ham_channels=256, + dropout_ratio=0.1, + num_classes=150, + norm_cfg=ham_norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + ham_kwargs=dict( + MD_S=1, + MD_R=16, + train_steps=6, + eval_steps=7, + inv_t=100, + rand_init=True)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) + +# dataset settings +train_dataloader = dict(batch_size=16) + +# optimizer +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict( + type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01), + paramwise_cfg=dict( + custom_keys={ + 'pos_block': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.), + 'head': dict(lr_mult=10.) + })) + +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + power=1.0, + begin=1500, + end=160000, + eta_min=0.0, + by_epoch=False, + ) +] diff --git a/configs/my_configs/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py b/configs/my_configs/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py new file mode 100644 index 0000000000..3a14da3ba1 --- /dev/null +++ b/configs/my_configs/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py @@ -0,0 +1,10 @@ +_base_ = '../bisenetv1/bisenetv1_r18-d32_4xb4-160k_coco-stuff164k-512x512.py' +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict( + backbone_cfg=dict( + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnet18_v1c'))), +) diff --git a/configs/my_configs/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py b/configs/my_configs/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py new file mode 100644 index 0000000000..5cea3371e9 --- /dev/null +++ b/configs/my_configs/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py @@ -0,0 +1,7 @@ +_base_ = '../bisenetv1/bisenetv1_r50-d32_4xb4-160k_coco-stuff164k-512x512.py' + +model = dict( + backbone=dict( + backbone_cfg=dict( + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnet50_v1c')))) diff --git a/configs/my_configs/ccnet_r101-d8_4xb4-40k_voc12aug-512x512.py b/configs/my_configs/ccnet_r101-d8_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..7963748d11 --- /dev/null +++ b/configs/my_configs/ccnet_r101-d8_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = '../ccnet/ccnet_r50-d8_4xb4-40k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/my_configs/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024.py b/configs/my_configs/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024.py new file mode 100644 index 0000000000..65b0ead547 --- /dev/null +++ b/configs/my_configs/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024.py @@ -0,0 +1,93 @@ +_base_ = [ + '../_base_/datasets/cityscapes_1024x1024.py', + '../_base_/default_runtime.py', +] + +# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa +# Licensed under the MIT License +class_weight = [ + 0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786, + 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529, + 1.0507 +] +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/pretrain/ddrnet23s-in1kpre_3rdparty-1ccac5b1.pth' # noqa +crop_size = (1024, 1024) +data_preprocessor = dict( + type='SegDataPreProcessor', + size=crop_size, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='DDRNet', + in_channels=3, + channels=32, + ppm_channels=128, + norm_cfg=norm_cfg, + align_corners=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), + decode_head=dict( + type='DDRHead', + in_channels=32 * 4, + channels=64, + dropout_ratio=0., + num_classes=19, + align_corners=False, + norm_cfg=norm_cfg, + loss_decode=[ + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + class_weight=class_weight, + loss_weight=1.0), + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + class_weight=class_weight, + loss_weight=0.4), + ]), + + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) + +train_dataloader = dict(batch_size=6, num_workers=4) + +iters = 120000 +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer, clip_grad=None) +# learning policy +param_scheduler = [ + dict( + type='PolyLR', + eta_min=0, + power=0.9, + begin=0, + end=iters, + by_epoch=False) +] + +# training schedule for 120k +train_cfg = dict( + type='IterBasedTrainLoop', max_iters=iters, val_interval=iters // 10) +val_cfg = dict(type='ValLoop') +test_cfg = dict(type='TestLoop') +default_hooks = dict( + timer=dict(type='IterTimerHook'), + logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), + param_scheduler=dict(type='ParamSchedulerHook'), + checkpoint=dict( + type='CheckpointHook', by_epoch=False, interval=iters // 10), + sampler_seed=dict(type='DistSamplerSeedHook'), + visualization=dict(type='SegVisualizationHook')) + +randomness = dict(seed=304) diff --git a/configs/my_configs/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024.py b/configs/my_configs/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024.py new file mode 100644 index 0000000000..444efe2b88 --- /dev/null +++ b/configs/my_configs/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024.py @@ -0,0 +1,93 @@ +_base_ = [ + '../_base_/datasets/cityscapes_1024x1024.py', + '../_base_/default_runtime.py', +] + +# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa +# Licensed under the MIT License +class_weight = [ + 0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786, + 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529, + 1.0507 +] +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/pretrain/ddrnet23-in1kpre_3rdparty-9ca29f62.pth' # noqa +crop_size = (1024, 1024) +data_preprocessor = dict( + type='SegDataPreProcessor', + size=crop_size, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='DDRNet', + in_channels=3, + channels=64, + ppm_channels=128, + norm_cfg=norm_cfg, + align_corners=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), + decode_head=dict( + type='DDRHead', + in_channels=64 * 4, + channels=128, + dropout_ratio=0., + num_classes=19, + align_corners=False, + norm_cfg=norm_cfg, + loss_decode=[ + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + class_weight=class_weight, + loss_weight=1.0), + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + class_weight=class_weight, + loss_weight=0.4), + ]), + + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) + +train_dataloader = dict(batch_size=6, num_workers=4) + +iters = 120000 +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer, clip_grad=None) +# learning policy +param_scheduler = [ + dict( + type='PolyLR', + eta_min=0, + power=0.9, + begin=0, + end=iters, + by_epoch=False) +] + +# training schedule for 120k +train_cfg = dict( + type='IterBasedTrainLoop', max_iters=iters, val_interval=iters // 10) +val_cfg = dict(type='ValLoop') +test_cfg = dict(type='TestLoop') +default_hooks = dict( + timer=dict(type='IterTimerHook'), + logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), + param_scheduler=dict(type='ParamSchedulerHook'), + checkpoint=dict( + type='CheckpointHook', by_epoch=False, interval=iters // 10), + sampler_seed=dict(type='DistSamplerSeedHook'), + visualization=dict(type='SegVisualizationHook')) + +randomness = dict(seed=304) diff --git a/configs/my_configs/deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512.py b/configs/my_configs/deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..0659669c90 --- /dev/null +++ b/configs/my_configs/deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = '../deeplabv3/deeplabv3_r50-d8_4xb4-20k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/my_configs/deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480.py b/configs/my_configs/deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480.py new file mode 100644 index 0000000000..5a882e16e3 --- /dev/null +++ b/configs/my_configs/deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480.py @@ -0,0 +1,2 @@ +_base_ = '../deeplabv3/deeplabv3_r50-d8_4xb4-40k_pascal-context-59-480x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/my_configs/deeplabv3_r18b-d8_4xb2-80k_cityscapes-512x1024.py b/configs/my_configs/deeplabv3_r18b-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..7ad6387591 --- /dev/null +++ b/configs/my_configs/deeplabv3_r18b-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,9 @@ +_base_ = '../deeplabv3/deeplabv3_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(type='ResNet', depth=18), + decode_head=dict( + in_channels=512, + channels=128, + ), + auxiliary_head=dict(in_channels=256, channels=64)) diff --git a/configs/my_configs/deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py b/configs/my_configs/deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py new file mode 100644 index 0000000000..c3b4f94f8c --- /dev/null +++ b/configs/my_configs/deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py @@ -0,0 +1,11 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', + '../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_320k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=171), + auxiliary_head=dict(num_classes=171)) diff --git a/configs/my_configs/deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512.py b/configs/my_configs/deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512.py new file mode 100644 index 0000000000..40dbffad45 --- /dev/null +++ b/configs/my_configs/deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512.py @@ -0,0 +1,11 @@ +_base_ = [ + '../_base_/models/deeplabv3_r50-d8.py', + '../_base_/datasets/coco-stuff10k.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_40k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=171), + auxiliary_head=dict(num_classes=171)) diff --git a/configs/my_configs/fast_scnn_8xb4-160k_cityscapes-512x1024.py b/configs/my_configs/fast_scnn_8xb4-160k_cityscapes-512x1024.py new file mode 100644 index 0000000000..e7f68bfe73 --- /dev/null +++ b/configs/my_configs/fast_scnn_8xb4-160k_cityscapes-512x1024.py @@ -0,0 +1,15 @@ +_base_ = [ + '../_base_/models/fast_scnn.py', '../_base_/datasets/cityscapes.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (512, 1024) +data_preprocessor = dict(size=crop_size) +model = dict(data_preprocessor=data_preprocessor) +# Re-config the data sampler. +train_dataloader = dict(batch_size=4, num_workers=4) +val_dataloader = dict(batch_size=1, num_workers=4) +test_dataloader = val_dataloader + +# Re-config the optimizer. +optimizer = dict(type='SGD', lr=0.12, momentum=0.9, weight_decay=4e-5) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer) diff --git a/configs/my_configs/fcn_hr18_4xb2-160k_cityscapes-512x1024.py b/configs/my_configs/fcn_hr18_4xb2-160k_cityscapes-512x1024.py new file mode 100644 index 0000000000..0b374632b8 --- /dev/null +++ b/configs/my_configs/fcn_hr18_4xb2-160k_cityscapes-512x1024.py @@ -0,0 +1,7 @@ +_base_ = [ + '../_base_/models/fcn_hr18.py', '../_base_/datasets/cityscapes.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (512, 1024) +data_preprocessor = dict(size=crop_size) +model = dict(data_preprocessor=data_preprocessor) diff --git a/configs/my_configs/fcn_hr18_4xb4-40k_voc12aug-512x512.py b/configs/my_configs/fcn_hr18_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..2aa16b124d --- /dev/null +++ b/configs/my_configs/fcn_hr18_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,8 @@ +_base_ = [ + '../_base_/models/fcn_hr18.py', '../_base_/datasets/pascal_voc12_aug.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, decode_head=dict(num_classes=21)) diff --git a/configs/my_configs/fcn_hr18s_4xb2-160k_cityscapes-512x1024.py b/configs/my_configs/fcn_hr18s_4xb2-160k_cityscapes-512x1024.py new file mode 100644 index 0000000000..02915e3d55 --- /dev/null +++ b/configs/my_configs/fcn_hr18s_4xb2-160k_cityscapes-512x1024.py @@ -0,0 +1,9 @@ +_base_ = '../hrnet/fcn_hr18_4xb2-160k_cityscapes-512x1024.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/my_configs/fcn_hr18s_4xb4-40k_voc12aug-512x512.py b/configs/my_configs/fcn_hr18s_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..73bf914a2a --- /dev/null +++ b/configs/my_configs/fcn_hr18s_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,9 @@ +_base_ = '../hrnet/fcn_hr18_4xb4-40k_voc12aug-512x512.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/my_configs/fcn_hr48_4xb4-80k_pascal-context-59-480x480.py b/configs/my_configs/fcn_hr48_4xb4-80k_pascal-context-59-480x480.py new file mode 100644 index 0000000000..4ac031ac42 --- /dev/null +++ b/configs/my_configs/fcn_hr48_4xb4-80k_pascal-context-59-480x480.py @@ -0,0 +1,10 @@ +_base_ = '../hrnet/fcn_hr18_4xb4-80k_pascal-context-59-480x480.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w48', + backbone=dict( + extra=dict( + stage2=dict(num_channels=(48, 96)), + stage3=dict(num_channels=(48, 96, 192)), + stage4=dict(num_channels=(48, 96, 192, 384)))), + decode_head=dict( + in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384]))) diff --git a/configs/my_configs/fcn_r50-d8_4xb4-20k_voc12aug-512x512.py b/configs/my_configs/fcn_r50-d8_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..eac86d5389 --- /dev/null +++ b/configs/my_configs/fcn_r50-d8_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,10 @@ +_base_ = [ + '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/pascal_voc12_aug.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=21), + auxiliary_head=dict(num_classes=21)) diff --git a/configs/my_configs/fpn_r50_4xb4-160k_ade20k-512x512.py b/configs/my_configs/fpn_r50_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..4e4bc57788 --- /dev/null +++ b/configs/my_configs/fpn_r50_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,8 @@ +_base_ = [ + '../_base_/models/fpn_r50.py', '../_base_/datasets/ade20k.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, decode_head=dict(num_classes=150)) diff --git a/configs/my_configs/gcnet_r50-d8_4xb4-20k_voc12aug-512x512.py b/configs/my_configs/gcnet_r50-d8_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..356b088236 --- /dev/null +++ b/configs/my_configs/gcnet_r50-d8_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,11 @@ +_base_ = [ + '../_base_/models/gcnet_r50-d8.py', + '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_20k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=21), + auxiliary_head=dict(num_classes=21)) diff --git a/configs/my_configs/icnet_r18-d8_4xb2-160k_cityscapes-832x832.py b/configs/my_configs/icnet_r18-d8_4xb2-160k_cityscapes-832x832.py new file mode 100644 index 0000000000..8765b9bf78 --- /dev/null +++ b/configs/my_configs/icnet_r18-d8_4xb2-160k_cityscapes-832x832.py @@ -0,0 +1,3 @@ +_base_ = '../icnet/icnet_r50-d8_4xb2-160k_cityscapes-832x832.py' +model = dict( + backbone=dict(layer_channels=(128, 512), backbone_cfg=dict(depth=18))) diff --git a/configs/my_configs/isanet_r101-d8_4xb4-20k_voc12aug-512x512.py b/configs/my_configs/isanet_r101-d8_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..1010b19f61 --- /dev/null +++ b/configs/my_configs/isanet_r101-d8_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = '../isanet/isanet_r50-d8_4xb4-20k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/my_configs/isanet_r50-d8_4xb4-20k_voc12aug-512x512.py b/configs/my_configs/isanet_r50-d8_4xb4-20k_voc12aug-512x512.py new file mode 100644 index 0000000000..591df42a87 --- /dev/null +++ b/configs/my_configs/isanet_r50-d8_4xb4-20k_voc12aug-512x512.py @@ -0,0 +1,11 @@ +_base_ = [ + '../_base_/models/isanet_r50-d8.py', + '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_20k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=21), + auxiliary_head=dict(num_classes=21)) diff --git a/configs/my_configs/mask2former_r101_8xb2-160k_ade20k-512x512.py b/configs/my_configs/mask2former_r101_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..6bec6d5fe4 --- /dev/null +++ b/configs/my_configs/mask2former_r101_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,7 @@ +_base_ = ['../mask2former/mask2former_r50_8xb2-160k_ade20k-512x512.py'] + +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/my_configs/mask2former_r50_8xb2-160k_ade20k-512x512.py b/configs/my_configs/mask2former_r50_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..78cf60510c --- /dev/null +++ b/configs/my_configs/mask2former_r50_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,200 @@ +_base_ = ['../_base_/default_runtime.py', '../_base_/datasets/ade20k.py'] + +custom_imports = dict(imports='mmdet.models', allow_failed_imports=False) + +crop_size = (512, 512) +data_preprocessor = dict( + type='SegDataPreProcessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255, + size=crop_size, + test_cfg=dict(size_divisor=32)) +num_classes = 150 +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='ResNet', + depth=50, + deep_stem=False, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=-1, + norm_cfg=dict(type='SyncBN', requires_grad=False), + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + decode_head=dict( + type='Mask2FormerHead', + in_channels=[256, 512, 1024, 2048], + strides=[4, 8, 16, 32], + feat_channels=256, + out_channels=256, + num_classes=num_classes, + num_queries=100, + num_transformer_feat_level=3, + align_corners=False, + pixel_decoder=dict( + type='mmdet.MSDeformAttnPixelDecoder', + num_outs=3, + norm_cfg=dict(type='GN', num_groups=32), + act_cfg=dict(type='ReLU'), + encoder=dict( # DeformableDetrTransformerEncoder + num_layers=6, + layer_cfg=dict( # DeformableDetrTransformerEncoderLayer + self_attn_cfg=dict( # MultiScaleDeformableAttention + embed_dims=256, + num_heads=8, + num_levels=3, + num_points=4, + im2col_step=64, + dropout=0.0, + batch_first=True, + norm_cfg=None, + init_cfg=None), + ffn_cfg=dict( + embed_dims=256, + feedforward_channels=1024, + num_fcs=2, + ffn_drop=0.0, + act_cfg=dict(type='ReLU', inplace=True))), + init_cfg=None), + positional_encoding=dict( # SinePositionalEncoding + num_feats=128, normalize=True), + init_cfg=None), + enforce_decoder_input_project=False, + positional_encoding=dict( # SinePositionalEncoding + num_feats=128, normalize=True), + transformer_decoder=dict( # Mask2FormerTransformerDecoder + return_intermediate=True, + num_layers=9, + layer_cfg=dict( # Mask2FormerTransformerDecoderLayer + self_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.0, + proj_drop=0.0, + dropout_layer=None, + batch_first=True), + cross_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.0, + proj_drop=0.0, + dropout_layer=None, + batch_first=True), + ffn_cfg=dict( + embed_dims=256, + feedforward_channels=2048, + num_fcs=2, + act_cfg=dict(type='ReLU', inplace=True), + ffn_drop=0.0, + dropout_layer=None, + add_identity=True)), + init_cfg=None), + loss_cls=dict( + type='mmdet.CrossEntropyLoss', + use_sigmoid=False, + loss_weight=2.0, + reduction='mean', + class_weight=[1.0] * num_classes + [0.1]), + loss_mask=dict( + type='mmdet.CrossEntropyLoss', + use_sigmoid=True, + reduction='mean', + loss_weight=5.0), + loss_dice=dict( + type='mmdet.DiceLoss', + use_sigmoid=True, + activate=True, + reduction='mean', + naive_dice=True, + eps=1.0, + loss_weight=5.0), + train_cfg=dict( + num_points=12544, + oversample_ratio=3.0, + importance_sample_ratio=0.75, + assigner=dict( + type='mmdet.HungarianAssigner', + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + weight=5.0, + use_sigmoid=True), + dict( + type='mmdet.DiceCost', + weight=5.0, + pred_act=True, + eps=1.0) + ]), + sampler=dict(type='mmdet.MaskPseudoSampler'))), + train_cfg=dict(), + test_cfg=dict(mode='whole')) + +# dataset config +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=True), + dict( + type='RandomChoiceResize', + scales=[int(512 * x * 0.1) for x in range(5, 21)], + resize_type='ResizeShortestEdge', + max_size=2048), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs') +] +train_dataloader = dict(batch_size=2, dataset=dict(pipeline=train_pipeline)) + +# optimizer +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +optimizer = dict( + type='AdamW', lr=0.0001, weight_decay=0.05, eps=1e-8, betas=(0.9, 0.999)) +optim_wrapper = dict( + type='OptimWrapper', + optimizer=optimizer, + clip_grad=dict(max_norm=0.01, norm_type=2), + paramwise_cfg=dict( + custom_keys={ + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi, + }, + norm_decay_mult=0.0)) +# learning policy +param_scheduler = [ + dict( + type='PolyLR', + eta_min=0, + power=0.9, + begin=0, + end=160000, + by_epoch=False) +] + +# training schedule for 160k +train_cfg = dict( + type='IterBasedTrainLoop', max_iters=160000, val_interval=5000) +val_cfg = dict(type='ValLoop') +test_cfg = dict(type='TestLoop') +default_hooks = dict( + timer=dict(type='IterTimerHook'), + logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), + param_scheduler=dict(type='ParamSchedulerHook'), + checkpoint=dict( + type='CheckpointHook', by_epoch=False, interval=5000, + save_best='mIoU'), + sampler_seed=dict(type='DistSamplerSeedHook'), + visualization=dict(type='SegVisualizationHook')) + +# Default setting for scaling LR automatically +# - `enable` means enable scaling LR automatically +# or not by default. +# - `base_batch_size` = (8 GPUs) x (2 samples per GPU). +auto_scale_lr = dict(enable=False, base_batch_size=16) diff --git a/configs/my_configs/mask2former_swin-s_8xb2-160k_ade20k-512x512.py b/configs/my_configs/mask2former_swin-s_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..78c4dfabb9 --- /dev/null +++ b/configs/my_configs/mask2former_swin-s_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,37 @@ +_base_ = ['../mask2former/mask2former_swin-t_8xb2-160k_ade20k-512x512.py'] +pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_small_patch4_window7_224_20220317-7ba6d6dd.pth' # noqa + +depths = [2, 2, 18, 2] +model = dict( + backbone=dict( + depths=depths, init_cfg=dict(type='Pretrained', + checkpoint=pretrained))) + +# set all layers in backbone to lr_mult=0.1 +# set all norm layers, position_embeding, +# query_embeding, level_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0) +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'absolute_pos_embed': backbone_embed_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optim_wrapper = dict( + paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0)) diff --git a/configs/my_configs/mask2former_swin-t_8xb2-160k_ade20k-512x512.py b/configs/my_configs/mask2former_swin-t_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..f08d9d61a4 --- /dev/null +++ b/configs/my_configs/mask2former_swin-t_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,52 @@ +_base_ = ['../mask2former/mask2former_r50_8xb2-160k_ade20k-512x512.py'] +pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' # noqa +depths = [2, 2, 6, 2] +model = dict( + backbone=dict( + _delete_=True, + type='SwinTransformer', + embed_dims=96, + depths=depths, + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.3, + patch_norm=True, + out_indices=(0, 1, 2, 3), + with_cp=False, + frozen_stages=-1, + init_cfg=dict(type='Pretrained', checkpoint=pretrained)), + decode_head=dict(in_channels=[96, 192, 384, 768])) + +# set all layers in backbone to lr_mult=0.1 +# set all norm layers, position_embeding, +# query_embeding, level_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0) +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'absolute_pos_embed': backbone_embed_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optim_wrapper = dict( + paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0)) diff --git a/configs/my_configs/maskformer_r101-d32_8xb2-160k_ade20k-512x512.py b/configs/my_configs/maskformer_r101-d32_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..a270436104 --- /dev/null +++ b/configs/my_configs/maskformer_r101-d32_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,7 @@ +_base_ = '../maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512.py' + +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/configs/my_configs/maskformer_r50-d32_8xb2-160k_ade20k-512x512.py b/configs/my_configs/maskformer_r50-d32_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..2a83746171 --- /dev/null +++ b/configs/my_configs/maskformer_r50-d32_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,141 @@ +_base_ = [ + '../_base_/datasets/ade20k.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_160k.py' +] +norm_cfg = dict(type='SyncBN', requires_grad=True) +crop_size = (512, 512) +data_preprocessor = dict( + type='SegDataPreProcessor', + size=crop_size, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255) +# model_cfg +num_classes = 150 +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + dilations=(1, 1, 1, 1), + strides=(1, 2, 2, 2), + norm_cfg=norm_cfg, + norm_eval=True, + style='pytorch', + contract_dilation=True, + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + decode_head=dict( + type='MaskFormerHead', + in_channels=[256, 512, 1024, + 2048], # input channels of pixel_decoder modules + feat_channels=256, + in_index=[0, 1, 2, 3], + num_classes=150, + out_channels=256, + num_queries=100, + pixel_decoder=dict( + type='mmdet.PixelDecoder', + norm_cfg=dict(type='GN', num_groups=32), + act_cfg=dict(type='ReLU')), + enforce_decoder_input_project=False, + positional_encoding=dict( # SinePositionalEncoding + num_feats=128, normalize=True), + transformer_decoder=dict( # DetrTransformerDecoder + return_intermediate=True, + num_layers=6, + layer_cfg=dict( # DetrTransformerDecoderLayer + self_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.1, + proj_drop=0.1, + dropout_layer=None, + batch_first=True), + cross_attn_cfg=dict( # MultiheadAttention + embed_dims=256, + num_heads=8, + attn_drop=0.1, + proj_drop=0.1, + dropout_layer=None, + batch_first=True), + ffn_cfg=dict( + embed_dims=256, + feedforward_channels=2048, + num_fcs=2, + act_cfg=dict(type='ReLU', inplace=True), + ffn_drop=0.1, + dropout_layer=None, + add_identity=True)), + init_cfg=None), + loss_cls=dict( + type='mmdet.CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0, + reduction='mean', + class_weight=[1.0] * num_classes + [0.1]), + loss_mask=dict( + type='mmdet.FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + reduction='mean', + loss_weight=20.0), + loss_dice=dict( + type='mmdet.DiceLoss', + use_sigmoid=True, + activate=True, + reduction='mean', + naive_dice=True, + eps=1.0, + loss_weight=1.0), + train_cfg=dict( + assigner=dict( + type='mmdet.HungarianAssigner', + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + type='mmdet.FocalLossCost', + weight=20.0, + binary_input=True), + dict( + type='mmdet.DiceCost', + weight=1.0, + pred_act=True, + eps=1.0) + ]), + sampler=dict(type='mmdet.MaskPseudoSampler'))), + # training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole'), +) +# optimizer +optimizer = dict( + type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.0001) +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=optimizer, + clip_grad=dict(max_norm=0.01, norm_type=2), + paramwise_cfg=dict(custom_keys={ + 'backbone': dict(lr_mult=0.1), + })) +# learning policy +param_scheduler = [ + dict( + type='PolyLR', + eta_min=0, + power=0.9, + begin=0, + end=160000, + by_epoch=False) +] + +# In MaskFormer implementation we use batch size 2 per GPU as default +train_dataloader = dict(batch_size=2, num_workers=2) +val_dataloader = dict(batch_size=1, num_workers=4) +test_dataloader = val_dataloader diff --git a/configs/my_configs/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py b/configs/my_configs/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..57d431c423 --- /dev/null +++ b/configs/my_configs/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,81 @@ +_base_ = '../maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512.py' + +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' # noqa +backbone_norm_cfg = dict(type='LN', requires_grad=True) +depths = [2, 2, 6, 2] +model = dict( + backbone=dict( + _delete_=True, + type='SwinTransformer', + pretrain_img_size=224, + embed_dims=96, + patch_size=4, + window_size=7, + mlp_ratio=4, + depths=depths, + num_heads=[3, 6, 12, 24], + strides=(4, 2, 2, 2), + out_indices=(0, 1, 2, 3), + qkv_bias=True, + qk_scale=None, + patch_norm=True, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.3, + use_abs_pos_embed=False, + act_cfg=dict(type='GELU'), + norm_cfg=backbone_norm_cfg, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file)), + decode_head=dict( + type='MaskFormerHead', + in_channels=[96, 192, 384, + 768], # input channels of pixel_decoder modules + )) + +# optimizer +optimizer = dict( + type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01) + +# set all layers in backbone to lr_mult=1.0 +# set all norm layers, position_embeding, +# query_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=1.0, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +embed_multi = dict(decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=optimizer, + clip_grad=dict(max_norm=0.01, norm_type=2), + paramwise_cfg=dict(custom_keys=custom_keys)) + +# learning policy +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + eta_min=0.0, + power=1.0, + begin=1500, + end=160000, + by_epoch=False, + ) +] diff --git a/configs/my_configs/mobilenet-v2-d8_fcn_4xb4-160k_ade20k-512x512.py b/configs/my_configs/mobilenet-v2-d8_fcn_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..015fa6f201 --- /dev/null +++ b/configs/my_configs/mobilenet-v2-d8_fcn_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,13 @@ +_base_ = '../fcn/fcn_r101-d8_4xb4-160k_ade20k-512x512.py' +model = dict( + pretrained='mmcls://mobilenet_v2', + backbone=dict( + _delete_=True, + type='MobileNetV2', + widen_factor=1., + strides=(1, 2, 2, 1, 1, 1, 1), + dilations=(1, 1, 1, 2, 2, 4, 4), + out_indices=(1, 2, 4, 6), + norm_cfg=dict(type='SyncBN', requires_grad=True)), + decode_head=dict(in_channels=320), + auxiliary_head=dict(in_channels=96)) diff --git a/configs/my_configs/mobilenet-v2-d8_pspnet_4xb4-160k_ade20k-512x512.py b/configs/my_configs/mobilenet-v2-d8_pspnet_4xb4-160k_ade20k-512x512.py new file mode 100644 index 0000000000..73db59beae --- /dev/null +++ b/configs/my_configs/mobilenet-v2-d8_pspnet_4xb4-160k_ade20k-512x512.py @@ -0,0 +1,13 @@ +_base_ = '../pspnet/pspnet_r101-d8_4xb4-160k_ade20k-512x512.py' +model = dict( + pretrained='mmcls://mobilenet_v2', + backbone=dict( + _delete_=True, + type='MobileNetV2', + widen_factor=1., + strides=(1, 2, 2, 1, 1, 1, 1), + dilations=(1, 1, 1, 2, 2, 4, 4), + out_indices=(1, 2, 4, 6), + norm_cfg=dict(type='SyncBN', requires_grad=True)), + decode_head=dict(in_channels=320), + auxiliary_head=dict(in_channels=96)) diff --git a/configs/my_configs/nonlocal_r101-d8_4xb4-40k_voc12aug-512x512.py b/configs/my_configs/nonlocal_r101-d8_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..8bc7045ef4 --- /dev/null +++ b/configs/my_configs/nonlocal_r101-d8_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = '../nonlocal_net/nonlocal_r50-d8_4xb4-40k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/my_configs/ocrnet_hr18_4xb4-40k_voc12aug-512x512.py b/configs/my_configs/ocrnet_hr18_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..c6b69ea632 --- /dev/null +++ b/configs/my_configs/ocrnet_hr18_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,40 @@ +_base_ = [ + '../_base_/models/ocrnet_hr18.py', + '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_40k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=[ + dict( + type='FCNHead', + in_channels=[18, 36, 72, 144], + channels=sum([18, 36, 72, 144]), + in_index=(0, 1, 2, 3), + input_transform='resize_concat', + kernel_size=1, + num_convs=1, + concat_input=False, + dropout_ratio=-1, + num_classes=21, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + dict( + type='OCRHead', + in_channels=[18, 36, 72, 144], + in_index=(0, 1, 2, 3), + input_transform='resize_concat', + channels=512, + ocr_channels=256, + dropout_ratio=-1, + num_classes=21, + norm_cfg=norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + ]) diff --git a/configs/my_configs/ocrnet_hr18s_4xb4-40k_voc12aug-512x512.py b/configs/my_configs/ocrnet_hr18s_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..b49b9c3282 --- /dev/null +++ b/configs/my_configs/ocrnet_hr18s_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,9 @@ +_base_ = '../ocrnet/ocrnet_hr18_4xb4-40k_voc12aug-512x512.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18_small', + backbone=dict( + extra=dict( + stage1=dict(num_blocks=(2, )), + stage2=dict(num_blocks=(2, 2)), + stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), + stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/configs/my_configs/pidnet-l_2xb6-120k_1024x1024-cityscapes.py b/configs/my_configs/pidnet-l_2xb6-120k_1024x1024-cityscapes.py new file mode 100644 index 0000000000..2d4073888d --- /dev/null +++ b/configs/my_configs/pidnet-l_2xb6-120k_1024x1024-cityscapes.py @@ -0,0 +1,10 @@ +_base_ = '../pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes.py' +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/pidnet/pidnet-l_imagenet1k_20230306-67889109.pth' # noqa +model = dict( + backbone=dict( + channels=64, + ppm_channels=112, + num_stem_blocks=3, + num_branch_blocks=4, + init_cfg=dict(checkpoint=checkpoint_file)), + decode_head=dict(in_channels=256, channels=256)) diff --git a/configs/my_configs/pidnet-m_2xb6-120k_1024x1024-cityscapes.py b/configs/my_configs/pidnet-m_2xb6-120k_1024x1024-cityscapes.py new file mode 100644 index 0000000000..cd881f3f5d --- /dev/null +++ b/configs/my_configs/pidnet-m_2xb6-120k_1024x1024-cityscapes.py @@ -0,0 +1,5 @@ +_base_ = '../pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes.py' +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/pidnet/pidnet-m_imagenet1k_20230306-39893c52.pth' # noqa +model = dict( + backbone=dict(channels=64, init_cfg=dict(checkpoint=checkpoint_file)), + decode_head=dict(in_channels=256)) diff --git a/configs/my_configs/pidnet-s_2xb6-120k_1024x1024-cityscapes.py b/configs/my_configs/pidnet-s_2xb6-120k_1024x1024-cityscapes.py new file mode 100644 index 0000000000..f70ca4287a --- /dev/null +++ b/configs/my_configs/pidnet-s_2xb6-120k_1024x1024-cityscapes.py @@ -0,0 +1,113 @@ +_base_ = [ + '../_base_/datasets/cityscapes_1024x1024.py', + '../_base_/default_runtime.py' +] + +# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa +# Licensed under the MIT License +class_weight = [ + 0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.8786, + 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955, 1.0865, 1.1529, + 1.0507 +] +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/pidnet/pidnet-s_imagenet1k_20230306-715e6273.pth' # noqa +crop_size = (1024, 1024) +data_preprocessor = dict( + type='SegDataPreProcessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255, + size=crop_size) +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + backbone=dict( + type='PIDNet', + in_channels=3, + channels=32, + ppm_channels=96, + num_stem_blocks=2, + num_branch_blocks=3, + align_corners=False, + norm_cfg=norm_cfg, + act_cfg=dict(type='ReLU', inplace=True), + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file)), + decode_head=dict( + type='PIDHead', + in_channels=128, + channels=128, + num_classes=19, + norm_cfg=norm_cfg, + act_cfg=dict(type='ReLU', inplace=True), + align_corners=True, + loss_decode=[ + dict( + type='CrossEntropyLoss', + use_sigmoid=False, + class_weight=class_weight, + loss_weight=0.4), + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + class_weight=class_weight, + loss_weight=1.0), + dict(type='BoundaryLoss', loss_weight=20.0), + dict( + type='OhemCrossEntropy', + thres=0.9, + min_kept=131072, + class_weight=class_weight, + loss_weight=1.0) + ]), + train_cfg=dict(), + test_cfg=dict(mode='whole')) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + type='RandomResize', + scale=(2048, 1024), + ratio_range=(0.5, 2.0), + keep_ratio=True), + dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict(type='GenerateEdge', edge_width=4), + dict(type='PackSegInputs') +] +train_dataloader = dict(batch_size=6, dataset=dict(pipeline=train_pipeline)) + +iters = 120000 +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer, clip_grad=None) +# learning policy +param_scheduler = [ + dict( + type='PolyLR', + eta_min=0, + power=0.9, + begin=0, + end=iters, + by_epoch=False) +] +# training schedule for 120k +train_cfg = dict( + type='IterBasedTrainLoop', max_iters=iters, val_interval=iters // 10) +val_cfg = dict(type='ValLoop') +test_cfg = dict(type='TestLoop') +default_hooks = dict( + timer=dict(type='IterTimerHook'), + logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), + param_scheduler=dict(type='ParamSchedulerHook'), + checkpoint=dict( + type='CheckpointHook', by_epoch=False, interval=iters // 10), + sampler_seed=dict(type='DistSamplerSeedHook'), + visualization=dict(type='SegVisualizationHook')) + +randomness = dict(seed=304) diff --git a/configs/my_configs/pspnet_r101-d8_4xb4-40k_voc12aug-512x512.py b/configs/my_configs/pspnet_r101-d8_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..43568a7799 --- /dev/null +++ b/configs/my_configs/pspnet_r101-d8_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,2 @@ +_base_ = '../pspnet/pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/my_configs/pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480.py b/configs/my_configs/pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480.py new file mode 100644 index 0000000000..eed77201d5 --- /dev/null +++ b/configs/my_configs/pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480.py @@ -0,0 +1,2 @@ +_base_ = '../pspnet/pspnet_r50-d8_4xb4-80k_pascal-context-59-480x480.py' +model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/configs/my_configs/pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024.py b/configs/my_configs/pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024.py new file mode 100644 index 0000000000..1f83edeaa2 --- /dev/null +++ b/configs/my_configs/pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024.py @@ -0,0 +1,9 @@ +_base_ = '../pspnet/pspnet_r50-d8_4xb2-80k_cityscapes-512x1024.py' +model = dict( + pretrained='torchvision://resnet18', + backbone=dict(type='ResNet', depth=18), + decode_head=dict( + in_channels=512, + channels=128, + ), + auxiliary_head=dict(in_channels=256, channels=64)) diff --git a/configs/my_configs/pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py b/configs/my_configs/pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py new file mode 100644 index 0000000000..2a9ce4c4f1 --- /dev/null +++ b/configs/my_configs/pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py @@ -0,0 +1,11 @@ +_base_ = [ + '../_base_/models/pspnet_r50-d8.py', + '../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_320k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=171), + auxiliary_head=dict(num_classes=171)) diff --git a/configs/my_configs/pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512.py b/configs/my_configs/pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512.py new file mode 100644 index 0000000000..fae57b0dbc --- /dev/null +++ b/configs/my_configs/pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512.py @@ -0,0 +1,10 @@ +_base_ = [ + '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/coco-stuff10k.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=171), + auxiliary_head=dict(num_classes=171)) diff --git a/configs/my_configs/pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py b/configs/my_configs/pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py new file mode 100644 index 0000000000..c4a4611530 --- /dev/null +++ b/configs/my_configs/pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py @@ -0,0 +1,11 @@ +_base_ = [ + '../_base_/models/pspnet_r50-d8.py', + '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_40k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=21), + auxiliary_head=dict(num_classes=21)) diff --git a/configs/my_configs/segformer_mit-b0_8xb2-160k_ade20k-512x512.py b/configs/my_configs/segformer_mit-b0_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..4a9476df68 --- /dev/null +++ b/configs/my_configs/segformer_mit-b0_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/segformer_mit-b0.py', '../_base_/datasets/ade20k.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' # noqa +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict(init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), + decode_head=dict(num_classes=150)) + +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict( + type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01), + paramwise_cfg=dict( + custom_keys={ + 'pos_block': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.), + 'head': dict(lr_mult=10.) + })) + +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + eta_min=0.0, + power=1.0, + begin=1500, + end=160000, + by_epoch=False, + ) +] +train_dataloader = dict(batch_size=2, num_workers=2) +val_dataloader = dict(batch_size=1, num_workers=4) +test_dataloader = val_dataloader diff --git a/configs/my_configs/segmenter_vit-s_fcn_8xb1-160k_ade20k-512x512.py b/configs/my_configs/segmenter_vit-s_fcn_8xb1-160k_ade20k-512x512.py new file mode 100644 index 0000000000..4e7ef59aa0 --- /dev/null +++ b/configs/my_configs/segmenter_vit-s_fcn_8xb1-160k_ade20k-512x512.py @@ -0,0 +1,14 @@ +_base_ = '../segmenter/segmenter_vit-s_mask_8xb1-160k_ade20k-512x512.py' + +model = dict( + decode_head=dict( + _delete_=True, + type='FCNHead', + in_channels=384, + channels=384, + num_convs=0, + dropout_ratio=0.0, + concat_input=False, + num_classes=150, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))) diff --git a/configs/my_configs/segmenter_vit-t_mask_8xb1-160k_ade20k-512x512.py b/configs/my_configs/segmenter_vit-t_mask_8xb1-160k_ade20k-512x512.py new file mode 100644 index 0000000000..221a9f9a6d --- /dev/null +++ b/configs/my_configs/segmenter_vit-t_mask_8xb1-160k_ade20k-512x512.py @@ -0,0 +1,26 @@ +_base_ = [ + '../_base_/models/segmenter_vit-b16_mask.py', + '../_base_/datasets/ade20k.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_160k.py' +] +crop_size = (512, 512) +data_preprocessor = dict(size=crop_size) +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segmenter/vit_tiny_p16_384_20220308-cce8c795.pth' # noqa + +model = dict( + data_preprocessor=data_preprocessor, + pretrained=checkpoint, + backbone=dict(embed_dims=192, num_heads=3), + decode_head=dict( + type='SegmenterMaskTransformerHead', + in_channels=192, + channels=192, + num_heads=3, + embed_dims=192)) + +optimizer = dict(lr=0.001, weight_decay=0.0) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer) +train_dataloader = dict( + # num_gpus: 8 -> batch_size: 8 + batch_size=1) +val_dataloader = dict(batch_size=1) diff --git a/configs/my_configs/segnext_mscan-b_1xb16-adamw-160k_ade20k-512x512.py b/configs/my_configs/segnext_mscan-b_1xb16-adamw-160k_ade20k-512x512.py new file mode 100644 index 0000000000..59adb822d8 --- /dev/null +++ b/configs/my_configs/segnext_mscan-b_1xb16-adamw-160k_ade20k-512x512.py @@ -0,0 +1,28 @@ +_base_ = '../segnext/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py' + +# model settings +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_b_20230227-3ab7d230.pth' # noqa +ham_norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + type='EncoderDecoder', + backbone=dict( + embed_dims=[64, 128, 320, 512], + depths=[3, 3, 12, 3], + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file), + drop_path_rate=0.1, + norm_cfg=dict(type='BN', requires_grad=True)), + decode_head=dict( + type='LightHamHead', + in_channels=[128, 320, 512], + in_index=[1, 2, 3], + channels=512, + ham_channels=512, + dropout_ratio=0.1, + num_classes=150, + norm_cfg=ham_norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) diff --git a/configs/my_configs/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py b/configs/my_configs/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py new file mode 100644 index 0000000000..c8d6da85ff --- /dev/null +++ b/configs/my_configs/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py @@ -0,0 +1,84 @@ +_base_ = [ + '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py', + '../_base_/datasets/ade20k.py' +] +# model settings +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' # noqa +ham_norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +crop_size = (512, 512) +data_preprocessor = dict( + type='SegDataPreProcessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True, + pad_val=0, + seg_pad_val=255, + size=(512, 512), + test_cfg=dict(size_divisor=32)) +model = dict( + type='EncoderDecoder', + data_preprocessor=data_preprocessor, + pretrained=None, + backbone=dict( + type='MSCAN', + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file), + embed_dims=[32, 64, 160, 256], + mlp_ratios=[8, 8, 4, 4], + drop_rate=0.0, + drop_path_rate=0.1, + depths=[3, 3, 5, 2], + attention_kernel_sizes=[5, [1, 7], [1, 11], [1, 21]], + attention_kernel_paddings=[2, [0, 3], [0, 5], [0, 10]], + act_cfg=dict(type='GELU'), + norm_cfg=dict(type='BN', requires_grad=True)), + decode_head=dict( + type='LightHamHead', + in_channels=[64, 160, 256], + in_index=[1, 2, 3], + channels=256, + ham_channels=256, + dropout_ratio=0.1, + num_classes=150, + norm_cfg=ham_norm_cfg, + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + ham_kwargs=dict( + MD_S=1, + MD_R=16, + train_steps=6, + eval_steps=7, + inv_t=100, + rand_init=True)), + # model training and testing settings + train_cfg=dict(), + test_cfg=dict(mode='whole')) + +# dataset settings +train_dataloader = dict(batch_size=16) + +# optimizer +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict( + type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01), + paramwise_cfg=dict( + custom_keys={ + 'pos_block': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.), + 'head': dict(lr_mult=10.) + })) + +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + power=1.0, + begin=1500, + end=160000, + eta_min=0.0, + by_epoch=False, + ) +] diff --git a/configs/poolformer/fpn_poolformer_s36_8x4_512x512_40k_ade20k.py b/configs/poolformer/fpn_poolformer_s36_8xb4-40k_ade20k-512x512.py similarity index 100% rename from configs/poolformer/fpn_poolformer_s36_8x4_512x512_40k_ade20k.py rename to configs/poolformer/fpn_poolformer_s36_8xb4-40k_ade20k-512x512.py diff --git a/configs/pspnet/pspnet_r18-d8_4xb2-20k_HOTS_v1-640x480.py b/configs/pspnet/pspnet_r18-d8_4xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..295b3e1381 --- /dev/null +++ b/configs/pspnet/pspnet_r18-d8_4xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,23 @@ +_base_ = [ + '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +num_classes = 46 +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + pretrained='open-mmlab://resnet18_v1c', + backbone=dict(depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=num_classes + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=num_classes + ) +) + diff --git a/configs/pspnet/pspnet_r18b-d8_4xb2-10k_cityscapes-512x1024.py b/configs/pspnet/pspnet_r18b-d8_4xb2-10k_cityscapes-512x1024.py new file mode 100644 index 0000000000..6fa10968e4 --- /dev/null +++ b/configs/pspnet/pspnet_r18b-d8_4xb2-10k_cityscapes-512x1024.py @@ -0,0 +1,18 @@ +_base_ = [ + '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_10k.py' +] + +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) + + +model = dict( + data_preprocessor=data_preprocessor, + pretrained='torchvision://resnet18', + backbone=dict(type='ResNet', depth=18), + decode_head=dict( + in_channels=512, + channels=128, + ), + auxiliary_head=dict(in_channels=256, channels=64)) diff --git a/configs/pspnet/pspnet_r18b-d8_4xb2-20k_HOTS_v1-640x480.py b/configs/pspnet/pspnet_r18b-d8_4xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..6a0a605095 --- /dev/null +++ b/configs/pspnet/pspnet_r18b-d8_4xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,24 @@ +_base_ = [ + '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] + +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) + +num_classes = 46 +model = dict( + data_preprocessor=data_preprocessor, + pretrained='torchvision://resnet18', + backbone=dict(type='ResNet', depth=18), + decode_head=dict( + in_channels=512, + channels=128, + num_classes=num_classes + ), + auxiliary_head=dict( + in_channels=256, + channels=64, + num_classes=num_classes + ) +) diff --git a/configs/pspnet/pspnet_r50-d32_4xb2-20k_HOTS_v1-640x480.py b/configs/pspnet/pspnet_r50-d32_4xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..3e1dd9c117 --- /dev/null +++ b/configs/pspnet/pspnet_r50-d32_4xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] + +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) + +num_classes = 46 +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict( + dilations=(1, 1, 2, 4), + strides=(1, 2, 2, 2) + ), + decode_head=dict( + num_classes=num_classes + ), + auxiliary_head=dict( + num_classes=num_classes + ) +) diff --git a/configs/pspnet/pspnet_r50-d32_rsb_4xb2-adamw-20k_HOTS_v1-640x480.py b/configs/pspnet/pspnet_r50-d32_rsb_4xb2-adamw-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..8eb584513b --- /dev/null +++ b/configs/pspnet/pspnet_r50-d32_rsb_4xb2-adamw-20k_HOTS_v1-640x480.py @@ -0,0 +1,48 @@ +_base_ = [ + '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] + +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +num_classes = 46 +checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa +model = dict( + data_preprocessor=data_preprocessor, + pretrained=None, + backbone=dict( + type='ResNet', + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint=checkpoint + ), + dilations=(1, 1, 2, 4), + strides=(1, 2, 2, 2) + ), + decode_head=dict( + num_classes=num_classes + ), + auxiliary_head=dict( + num_classes=num_classes + ) +) + +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=0.0005, weight_decay=0.05), + clip_grad=dict(max_norm=1, norm_type=2)) +# learning policy +param_scheduler = [ + dict( + type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, + end=1000), + dict( + type='MultiStepLR', + begin=1000, + end=20000, + by_epoch=False, + milestones=[15000, 17000], + ) +] diff --git a/configs/pspnet/pspnet_r50b-d32_4xb2-20k_HOTS_v1-640x480.py b/configs/pspnet/pspnet_r50b-d32_4xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..4de889bae2 --- /dev/null +++ b/configs/pspnet/pspnet_r50b-d32_4xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,23 @@ +_base_ = [ + '../_base_/models/pspnet_r50-d8.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] + +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +num_classes = 46 +model = dict( + data_preprocessor=data_preprocessor, + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + dilations=(1, 1, 2, 4), + strides=(1, 2, 2, 2) + ), + decode_head=dict( + num_classes=num_classes + ), + auxiliary_head=dict( + num_classes=num_classes + ) +) diff --git a/configs/segformer/segformer_mit-b0_8xb2-20k_HOTS_v1-640x480.py b/configs/segformer/segformer_mit-b0_8xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..1773694c13 --- /dev/null +++ b/configs/segformer/segformer_mit-b0_8xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/segformer_mit-b0.py', '../_base_/datasets/hots_v1_640x480.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' # noqa +model = dict( + data_preprocessor=data_preprocessor, + backbone=dict(init_cfg=dict(type='Pretrained', checkpoint=checkpoint)), + decode_head=dict(num_classes=46)) + +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict( + type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01), + paramwise_cfg=dict( + custom_keys={ + 'pos_block': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.), + 'head': dict(lr_mult=10.) + })) + +param_scheduler = [ + dict( + type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), + dict( + type='PolyLR', + eta_min=0.0, + power=1.0, + begin=1500, + end=20000, + by_epoch=False, + ) +] +train_dataloader = dict(batch_size=1, num_workers=2) +val_dataloader = dict(batch_size=1, num_workers=4) +test_dataloader = val_dataloader diff --git a/configs/segformer/segformer_mit-b1_8xb2-20k_HOTS_v1-640x480.py b/configs/segformer/segformer_mit-b1_8xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..8e2ac3be70 --- /dev/null +++ b/configs/segformer/segformer_mit-b1_8xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,12 @@ +_base_ = ['./segformer_mit-b0_8xb2-20k_HOTS_v1-640x480.py'] + +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b1_20220624-02e5a6a1.pth' # noqa + +# model settings +model = dict( + backbone=dict( + init_cfg=dict(type='Pretrained', checkpoint=checkpoint), + embed_dims=64, + num_heads=[1, 2, 5, 8], + num_layers=[2, 2, 2, 2]), + decode_head=dict(in_channels=[64, 128, 320, 512])) diff --git a/configs/segformer/segformer_mit-b2_8xb2-20k_HOTS_v1-640x480.py b/configs/segformer/segformer_mit-b2_8xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..824139f5c4 --- /dev/null +++ b/configs/segformer/segformer_mit-b2_8xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,12 @@ +_base_ = ['./segformer_mit-b0_8xb2-20k_HOTS_v1-640x480.py'] + +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b2_20220624-66e8bf70.pth' # noqa + +# model settings +model = dict( + backbone=dict( + init_cfg=dict(type='Pretrained', checkpoint=checkpoint), + embed_dims=64, + num_heads=[1, 2, 5, 8], + num_layers=[3, 4, 6, 3]), + decode_head=dict(in_channels=[64, 128, 320, 512])) diff --git a/configs/segformer/segformer_mit-b3_8xb2-20k_HOTS_v1-640x480.py b/configs/segformer/segformer_mit-b3_8xb2-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..a50ea0f48e --- /dev/null +++ b/configs/segformer/segformer_mit-b3_8xb2-20k_HOTS_v1-640x480.py @@ -0,0 +1,12 @@ +_base_ = ['./segformer_mit-b0_8xb2-20k_HOTS_v1-640x480.py'] + +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b3_20220624-13b1141c.pth' # noqa + +# model settings +model = dict( + backbone=dict( + init_cfg=dict(type='Pretrained', checkpoint=checkpoint), + embed_dims=64, + num_heads=[1, 2, 5, 8], + num_layers=[3, 4, 18, 3]), + decode_head=dict(in_channels=[64, 128, 320, 512])) diff --git a/configs/segmenter/segmenter_vit-b_mask_8xb1-20k_HOTS_v1-640x480.py b/configs/segmenter/segmenter_vit-b_mask_8xb1-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..eba13b0be1 --- /dev/null +++ b/configs/segmenter/segmenter_vit-b_mask_8xb1-20k_HOTS_v1-640x480.py @@ -0,0 +1,17 @@ +_base_ = [ + '../_base_/models/segmenter_vit-b16_mask.py', + '../_base_/datasets/hots_v1_640x480.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +model = dict( + data_preprocessor=data_preprocessor, + decode_head=dict(num_classes=46) + ) +optimizer = dict(lr=0.001, weight_decay=0.0) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer) +train_dataloader = dict( + # num_gpus: 8 -> batch_size: 8 + batch_size=1) +val_dataloader = dict(batch_size=1) diff --git a/configs/segmenter/segmenter_vit-s_fcn_8xb1-20k_HOTS_v1-640x480.py b/configs/segmenter/segmenter_vit-s_fcn_8xb1-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..a6de69b829 --- /dev/null +++ b/configs/segmenter/segmenter_vit-s_fcn_8xb1-20k_HOTS_v1-640x480.py @@ -0,0 +1,14 @@ +_base_ = './segmenter_vit-s_mask_8xb1-20k_HOTS_v1-640x480.py' + +model = dict( + decode_head=dict( + _delete_=True, + type='FCNHead', + in_channels=384, + channels=384, + num_convs=0, + dropout_ratio=0.0, + concat_input=False, + num_classes=46, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))) diff --git a/configs/segmenter/segmenter_vit-s_mask_8xb1-20k_HOTS_v1-640x480.py b/configs/segmenter/segmenter_vit-s_mask_8xb1-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..fd99d80eb9 --- /dev/null +++ b/configs/segmenter/segmenter_vit-s_mask_8xb1-20k_HOTS_v1-640x480.py @@ -0,0 +1,36 @@ +_base_ = [ + '../_base_/models/segmenter_vit-b16_mask.py', + '../_base_/datasets/hots_v1_640x480.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segmenter/vit_small_p16_384_20220308-410f6037.pth' # noqa + +backbone_norm_cfg = dict(type='LN', eps=1e-6, requires_grad=True) +model = dict( + data_preprocessor=data_preprocessor, + pretrained=checkpoint, + backbone=dict( + img_size=(640, 480), + embed_dims=384, + num_heads=6, + ), + decode_head=dict( + type='SegmenterMaskTransformerHead', + in_channels=384, + channels=384, + num_classes=46, + num_layers=2, + num_heads=6, + embed_dims=384, + dropout_ratio=0.0, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))) + +optimizer = dict(lr=0.001, weight_decay=0.0) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer) +train_dataloader = dict( + # num_gpus: 8 -> batch_size: 8 + batch_size=1) +val_dataloader = dict(batch_size=1) diff --git a/configs/segmenter/segmenter_vit-t_mask_8xb1-20k_HOTS_v1-640x480.py b/configs/segmenter/segmenter_vit-t_mask_8xb1-20k_HOTS_v1-640x480.py new file mode 100644 index 0000000000..74f4c37c82 --- /dev/null +++ b/configs/segmenter/segmenter_vit-t_mask_8xb1-20k_HOTS_v1-640x480.py @@ -0,0 +1,28 @@ +_base_ = [ + '../_base_/models/segmenter_vit-b16_mask.py', + '../_base_/datasets/hots_v1_640x480.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_20k.py' +] +crop_size = (640, 480) +data_preprocessor = dict(size=crop_size) +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segmenter/vit_tiny_p16_384_20220308-cce8c795.pth' # noqa + +model = dict( + data_preprocessor=data_preprocessor, + pretrained=checkpoint, + backbone=dict(embed_dims=192, num_heads=3), + decode_head=dict( + type='SegmenterMaskTransformerHead', + in_channels=192, + channels=192, + num_heads=3, + embed_dims=192, + num_classes=46) + ) + +optimizer = dict(lr=0.001, weight_decay=0.0) +optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer) +train_dataloader = dict( + # num_gpus: 8 -> batch_size: 8 + batch_size=1) +val_dataloader = dict(batch_size=1) diff --git a/configs/setr/setr_vit-l-mla_8xb1-160k_ade20k-512x512.py b/configs/setr/setr_vit-l-mla_8xb1-160k_ade20k-512x512.py index 1c6e2845f9..e114faafe8 100644 --- a/configs/setr/setr_vit-l-mla_8xb1-160k_ade20k-512x512.py +++ b/configs/setr/setr_vit-l-mla_8xb1-160k_ade20k-512x512.py @@ -12,7 +12,7 @@ img_size=(512, 512), drop_rate=0., init_cfg=dict( - type='Pretrained', checkpoint='pretrain/vit_large_p16.pth')), + type='Pretrained', checkpoint="checkpoints/vit-large-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-b20ba619.pth")), decode_head=dict(num_classes=150), auxiliary_head=[ dict( diff --git a/configs/setr/setr_vit-l_mla_8xb1-80k_cityscapes-768x768.py b/configs/setr/setr_vit-l_mla_8xb1-80k_cityscapes-768x768.py index 026557f505..fd36670f1d 100644 --- a/configs/setr/setr_vit-l_mla_8xb1-80k_cityscapes-768x768.py +++ b/configs/setr/setr_vit-l_mla_8xb1-80k_cityscapes-768x768.py @@ -2,6 +2,7 @@ '../_base_/models/setr_mla.py', '../_base_/datasets/cityscapes_768x768.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' ] + crop_size = (768, 768) data_preprocessor = dict(size=crop_size) model = dict( @@ -10,7 +11,7 @@ backbone=dict( drop_rate=0, init_cfg=dict( - type='Pretrained', checkpoint='pretrain/vit_large_p16.pth')), + type='Pretrained', checkpoint="checkpoints/vit-large-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-b20ba619.pth")), test_cfg=dict(mode='slide', crop_size=(768, 768), stride=(512, 512))) optimizer = dict(lr=0.002, weight_decay=0.0) diff --git a/configs/setr/setr_vit-l_naive_8xb1-80k_cityscapes-768x768.py b/configs/setr/setr_vit-l_naive_8xb1-80k_cityscapes-768x768.py index db49317301..594a69e309 100644 --- a/configs/setr/setr_vit-l_naive_8xb1-80k_cityscapes-768x768.py +++ b/configs/setr/setr_vit-l_naive_8xb1-80k_cityscapes-768x768.py @@ -3,6 +3,7 @@ '../_base_/datasets/cityscapes_768x768.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' ] + crop_size = (768, 768) data_preprocessor = dict(size=crop_size) model = dict( @@ -11,7 +12,7 @@ backbone=dict( drop_rate=0., init_cfg=dict( - type='Pretrained', checkpoint='pretrain/vit_large_p16.pth')), + type='Pretrained', checkpoint="checkpoints/vit-large-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-b20ba619.pth")), test_cfg=dict(mode='slide', crop_size=(768, 768), stride=(512, 512))) optimizer = dict(weight_decay=0.0) diff --git a/configs/setr/setr_vit-l_naive_8xb2-160k_ade20k-512x512.py b/configs/setr/setr_vit-l_naive_8xb2-160k_ade20k-512x512.py index 109996c1b6..b0729c867d 100644 --- a/configs/setr/setr_vit-l_naive_8xb2-160k_ade20k-512x512.py +++ b/configs/setr/setr_vit-l_naive_8xb2-160k_ade20k-512x512.py @@ -2,6 +2,7 @@ '../_base_/models/setr_naive.py', '../_base_/datasets/ade20k.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' ] + crop_size = (512, 512) data_preprocessor = dict(size=crop_size) norm_cfg = dict(type='SyncBN', requires_grad=True) @@ -12,7 +13,7 @@ img_size=(512, 512), drop_rate=0., init_cfg=dict( - type='Pretrained', checkpoint='pretrain/vit_large_p16.pth')), + type='Pretrained', checkpoint="checkpoints/vit-large-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-b20ba619.pth")), decode_head=dict(num_classes=150), auxiliary_head=[ dict( diff --git a/configs/setr/setr_vit-l_pup_8xb1-80k_cityscapes-768x768.py b/configs/setr/setr_vit-l_pup_8xb1-80k_cityscapes-768x768.py index 999ab18038..7b8f75bf25 100644 --- a/configs/setr/setr_vit-l_pup_8xb1-80k_cityscapes-768x768.py +++ b/configs/setr/setr_vit-l_pup_8xb1-80k_cityscapes-768x768.py @@ -12,7 +12,7 @@ backbone=dict( drop_rate=0., init_cfg=dict( - type='Pretrained', checkpoint='pretrain/vit_large_p16.pth')), + type='Pretrained', checkpoint="checkpoints/vit-large-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-b20ba619.pth")), auxiliary_head=[ dict( type='SETRUPHead', diff --git a/configs/setr/setr_vit-l_pup_8xb2-160k_ade20k-512x512.py b/configs/setr/setr_vit-l_pup_8xb2-160k_ade20k-512x512.py index e9bfb2201d..433a5a221f 100644 --- a/configs/setr/setr_vit-l_pup_8xb2-160k_ade20k-512x512.py +++ b/configs/setr/setr_vit-l_pup_8xb2-160k_ade20k-512x512.py @@ -12,7 +12,7 @@ img_size=(512, 512), drop_rate=0., init_cfg=dict( - type='Pretrained', checkpoint='pretrain/vit_large_p16.pth')), + type='Pretrained', checkpoint="checkpoints/vit-large-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-b20ba619.pth")), decode_head=dict(num_classes=150), auxiliary_head=[ dict( diff --git a/configs/vit/vit_deit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py b/configs/vit/vit_deit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py index 39d1c54faf..3f88318419 100644 --- a/configs/vit/vit_deit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py +++ b/configs/vit/vit_deit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py @@ -1,5 +1,5 @@ _base_ = './vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py' model = dict( - pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth', + # pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth', backbone=dict(drop_path_rate=0.1, final_norm=True)) diff --git a/configs/vit/vit_deit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py b/configs/vit/vit_deit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py index 706673f6b1..973aed6a3a 100644 --- a/configs/vit/vit_deit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py +++ b/configs/vit/vit_deit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py @@ -1,6 +1,6 @@ _base_ = './vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py' model = dict( - pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth', - backbone=dict(drop_path_rate=0.1), + # pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth', + backbone=dict(drop_path_rate=0.1) ) diff --git a/configs/vit/vit_deit-b16_upernet_8xb2-160k_ade20k-512x512.py b/configs/vit/vit_deit-b16_upernet_8xb2-160k_ade20k-512x512.py index 23a23582d7..793c5b66aa 100644 --- a/configs/vit/vit_deit-b16_upernet_8xb2-160k_ade20k-512x512.py +++ b/configs/vit/vit_deit-b16_upernet_8xb2-160k_ade20k-512x512.py @@ -1,6 +1,6 @@ _base_ = './vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py' model = dict( - pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth', + # pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth', backbone=dict(drop_path_rate=0.1), neck=None) diff --git a/configs/vit/vit_deit-b16_upernet_8xb2-80k_ade20k-512x512.py b/configs/vit/vit_deit-b16_upernet_8xb2-80k_ade20k-512x512.py index 4c8bc939ee..0a3f6f5d4c 100644 --- a/configs/vit/vit_deit-b16_upernet_8xb2-80k_ade20k-512x512.py +++ b/configs/vit/vit_deit-b16_upernet_8xb2-80k_ade20k-512x512.py @@ -1,6 +1,6 @@ _base_ = './vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py' model = dict( - pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth', + # pretrained='pretrain/deit_base_patch16_224-b5f2ef4d.pth', backbone=dict(drop_path_rate=0.1), neck=None) diff --git a/configs/vit/vit_deit-s16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py b/configs/vit/vit_deit-s16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py index 8e626fe0de..47fe692b9d 100644 --- a/configs/vit/vit_deit-s16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py +++ b/configs/vit/vit_deit-s16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py @@ -1,7 +1,7 @@ _base_ = './vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py' model = dict( - pretrained='pretrain/deit_small_patch16_224-cd65a155.pth', + # pretrained='pretrain/deit_small_patch16_224-cd65a155.pth', backbone=dict( num_heads=6, embed_dims=384, drop_path_rate=0.1, final_norm=True), decode_head=dict(num_classes=150, in_channels=[384, 384, 384, 384]), diff --git a/configs/vit/vit_deit-s16_mln_upernet_8xb2-160k_ade20k-512x512.py b/configs/vit/vit_deit-s16_mln_upernet_8xb2-160k_ade20k-512x512.py index 9a69a892b3..d02afb589a 100644 --- a/configs/vit/vit_deit-s16_mln_upernet_8xb2-160k_ade20k-512x512.py +++ b/configs/vit/vit_deit-s16_mln_upernet_8xb2-160k_ade20k-512x512.py @@ -1,7 +1,7 @@ _base_ = './vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py' model = dict( - pretrained='pretrain/deit_small_patch16_224-cd65a155.pth', + # pretrained='pretrain/deit_small_patch16_224-cd65a155.pth', backbone=dict(num_heads=6, embed_dims=384, drop_path_rate=0.1), decode_head=dict(num_classes=150, in_channels=[384, 384, 384, 384]), neck=dict(in_channels=[384, 384, 384, 384], out_channels=384), diff --git a/configs/vit/vit_deit-s16_upernet_8xb2-160k_ade20k-512x512.py b/configs/vit/vit_deit-s16_upernet_8xb2-160k_ade20k-512x512.py index 9ef699d5d5..3e7bdb6e95 100644 --- a/configs/vit/vit_deit-s16_upernet_8xb2-160k_ade20k-512x512.py +++ b/configs/vit/vit_deit-s16_upernet_8xb2-160k_ade20k-512x512.py @@ -1,7 +1,7 @@ _base_ = './vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py' model = dict( - pretrained='pretrain/deit_small_patch16_224-cd65a155.pth', + # pretrained='pretrain/deit_small_patch16_224-cd65a155.pth', backbone=dict(num_heads=6, embed_dims=384, drop_path_rate=0.1), decode_head=dict(num_classes=150, in_channels=[384, 384, 384, 384]), neck=None, diff --git a/configs/vit/vit_deit-s16_upernet_8xb2-80k_ade20k-512x512.py b/configs/vit/vit_deit-s16_upernet_8xb2-80k_ade20k-512x512.py index 9ef699d5d5..3e7bdb6e95 100644 --- a/configs/vit/vit_deit-s16_upernet_8xb2-80k_ade20k-512x512.py +++ b/configs/vit/vit_deit-s16_upernet_8xb2-80k_ade20k-512x512.py @@ -1,7 +1,7 @@ _base_ = './vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py' model = dict( - pretrained='pretrain/deit_small_patch16_224-cd65a155.pth', + # pretrained='pretrain/deit_small_patch16_224-cd65a155.pth', backbone=dict(num_heads=6, embed_dims=384, drop_path_rate=0.1), decode_head=dict(num_classes=150, in_channels=[384, 384, 384, 384]), neck=None, diff --git a/configs/vit/vit_vit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py b/configs/vit/vit_vit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py index 2dd81b48bb..e7ca4e2051 100644 --- a/configs/vit/vit_vit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py +++ b/configs/vit/vit_vit-b16-ln_mln_upernet_8xb2-160k_ade20k-512x512.py @@ -7,7 +7,7 @@ data_preprocessor = dict(size=crop_size) model = dict( data_preprocessor=data_preprocessor, - pretrained='pretrain/vit_base_patch16_224.pth', + # pretrained='pretrain/vit_base_patch16_224.pth', backbone=dict(drop_path_rate=0.1, final_norm=True), decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150)) diff --git a/configs/vit/vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py b/configs/vit/vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py index 1a7ec16c92..91f12c3b6e 100644 --- a/configs/vit/vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py +++ b/configs/vit/vit_vit-b16_mln_upernet_8xb2-160k_ade20k-512x512.py @@ -7,7 +7,7 @@ data_preprocessor = dict(size=crop_size) model = dict( data_preprocessor=data_preprocessor, - pretrained='pretrain/vit_base_patch16_224.pth', + # pretrained='pretrain/vit_base_patch16_224.pth', decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150)) diff --git a/configs/vit/vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py b/configs/vit/vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py index ef7345057c..d5952aee45 100644 --- a/configs/vit/vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py +++ b/configs/vit/vit_vit-b16_mln_upernet_8xb2-80k_ade20k-512x512.py @@ -7,7 +7,7 @@ data_preprocessor = dict(size=crop_size) model = dict( data_preprocessor=data_preprocessor, - pretrained='pretrain/vit_base_patch16_224.pth', + # pretrained='pretrain/vit_base_patch16_224.pth', decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150)) diff --git a/example.py b/example.py new file mode 100644 index 0000000000..09be1b8b0e --- /dev/null +++ b/example.py @@ -0,0 +1,280 @@ +crop_size = ( + 512, + 1024, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cityscapes/' +dataset_type = 'CityscapesDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=8000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=1024, + in_index=2, + loss_decode=dict( + loss_weight=0.4, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=19, + num_convs=1, + type='FCNHead'), + backbone=dict( + contract_dilation=True, + depth=50, + dilations=( + 1, + 1, + 2, + 4, + ), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 1, + 1, + ), + style='pytorch', + type='ResNetV1c'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=512, + concat_input=True, + dropout_ratio=0.1, + in_channels=2048, + in_index=3, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=19, + num_convs=2, + type='FCNHead'), + pretrained='open-mmlab://resnet50_v1c', + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=80000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='leftImg8bit/val', seg_map_path='gtFine/val'), + data_root='data/cityscapes/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 1024, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='CityscapesDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 1024, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=200, type='IterBasedTrainLoop', val_interval=8000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='leftImg8bit/train', seg_map_path='gtFine/train'), + data_root='data/cityscapes/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 1024, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 1024, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='CityscapesDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 1024, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 1024, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='leftImg8bit/val', seg_map_path='gtFine/val'), + data_root='data/cityscapes/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 1024, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='CityscapesDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) diff --git a/mmseg/datasets/__init__.py b/mmseg/datasets/__init__.py index f8ad750d76..1ea9195cae 100644 --- a/mmseg/datasets/__init__.py +++ b/mmseg/datasets/__init__.py @@ -26,6 +26,14 @@ from .refuge import REFUGEDataset from .stare import STAREDataset from .synapse import SynapseDataset +from .hots_v1 import HOTSDataset +from .hots_v1_cat import HOTSCATDataset +from .irl_vision_sim import IRLVisionSimDataset +from .irl_vision_sim_cat import IRLVisionSimCATDataset +from .arid20_cat import ARID20CATDataset +from .arid10_cat import ARID10CATDataset +from .sodhots_c import SODHOTSCDataset + # yapf: disable from .transforms import (CLAHE, AdjustGamma, Albu, BioMedical3DPad, BioMedical3DRandomCrop, BioMedical3DRandomFlip, @@ -61,5 +69,7 @@ 'MapillaryDataset_v2', 'Albu', 'LEVIRCDDataset', 'LoadMultipleRSImageFromFile', 'LoadSingleRSImageFromFile', 'ConcatCDInput', 'BaseCDDataset', 'DSDLSegDataset', 'BDD100KDataset', - 'NYUDataset', 'HSIDrive20Dataset' + 'NYUDataset', 'HSIDrive20Dataset', 'HOTSDataset', 'HOTSCATDataset', + 'IRLVisionSimDataset', 'IRLVisionSimCATDataset' + 'ARID20CATDataset', 'SODHOTSCDataset', 'ARID10CATDataset' ] diff --git a/mmseg/datasets/arid10_cat.py b/mmseg/datasets/arid10_cat.py new file mode 100644 index 0000000000..a9844517ff --- /dev/null +++ b/mmseg/datasets/arid10_cat.py @@ -0,0 +1,45 @@ +from mmseg.registry import DATASETS +from .basesegdataset import BaseSegDataset + + +@DATASETS.register_module() +class ARID10CATDataset(BaseSegDataset): + + METAINFO = dict( + classes=( + "_background_", "apple", "ball", "banana", + "bell_pepper", "binder", "bowl", "cereal_box", + "coffee_mug", "flashlight", "food_bag", "food_box", + "food_can", "glue_stick", "hand_towel", "instant_noodles", + "keyboard", "kleenex", "lemon", "lime", + "marker", "orange", "peach", "pear", + "potato", "shampoo", "soda_can", "sponge", + "stapler", "tomato", "toothpaste", "_unknown_" + ), + palette=[ + [0, 0, 0], [69, 167, 125], [98, 36, 68], [166, 50, 141], + [127, 254, 225], [255, 243, 59], [53, 166, 152], [61, 228, 157], + [22, 187, 196], [33, 15, 250], [234, 19, 6], [95, 152, 115], + [255, 120, 0], [160, 54, 224], [252, 77, 216], [255, 0, 200], + [168, 175, 110], [190, 90, 100], [197, 39, 15], [137, 233, 180], + [233, 229, 20], [30, 241, 158], [137, 34, 172], [21, 154, 206], + [104, 190, 112], [219, 205, 202], [96, 129, 51], [32, 203, 104], + [29, 241, 69], [35, 229, 130], [124, 162, 250], [163, 156, 156] + ] + ) + + def __init__(self, + img_suffix='.png', + seg_map_suffix='.png', + reduce_zero_label=False, + **kwargs) -> None: + super().__init__( + img_suffix=img_suffix, + seg_map_suffix=seg_map_suffix, + reduce_zero_label=reduce_zero_label, + **kwargs) + + + + + diff --git a/mmseg/datasets/arid20_cat.py b/mmseg/datasets/arid20_cat.py new file mode 100644 index 0000000000..111c3c235e --- /dev/null +++ b/mmseg/datasets/arid20_cat.py @@ -0,0 +1,45 @@ +from mmseg.registry import DATASETS +from .basesegdataset import BaseSegDataset + + +@DATASETS.register_module() +class ARID20CATDataset(BaseSegDataset): + + METAINFO = dict( + classes=( + "_background_", "apple", "ball", "banana", + "bell_pepper", "binder", "bowl", "cereal_box", + "coffee_mug", "flashlight", "food_bag", "food_box", + "food_can", "glue_stick", "hand_towel", "instant_noodles", + "keyboard", "kleenex", "lemon", "lime", + "marker", "orange", "peach", "pear", + "potato", "shampoo", "soda_can", "sponge", + "stapler", "tomato", "toothpaste", "_unknown_" + ), + palette=[ + [0, 0, 0], [69, 167, 125], [98, 36, 68], [166, 50, 141], + [127, 254, 225], [255, 243, 59], [53, 166, 152], [61, 228, 157], + [22, 187, 196], [33, 15, 250], [234, 19, 6], [95, 152, 115], + [255, 120, 0], [160, 54, 224], [252, 77, 216], [255, 0, 200], + [168, 175, 110], [190, 90, 100], [197, 39, 15], [137, 233, 180], + [233, 229, 20], [30, 241, 158], [137, 34, 172], [21, 154, 206], + [104, 190, 112], [219, 205, 202], [96, 129, 51], [32, 203, 104], + [29, 241, 69], [35, 229, 130], [124, 162, 250], [163, 156, 156] + ] + ) + + def __init__(self, + img_suffix='.png', + seg_map_suffix='.png', + reduce_zero_label=False, + **kwargs) -> None: + super().__init__( + img_suffix=img_suffix, + seg_map_suffix=seg_map_suffix, + reduce_zero_label=reduce_zero_label, + **kwargs) + + + + + diff --git a/mmseg/datasets/hots_v1.py b/mmseg/datasets/hots_v1.py new file mode 100644 index 0000000000..f8edb79a89 --- /dev/null +++ b/mmseg/datasets/hots_v1.py @@ -0,0 +1,48 @@ +from mmseg.registry import DATASETS +from .basesegdataset import BaseSegDataset + + +@DATASETS.register_module() +class HOTSDataset(BaseSegDataset): + + METAINFO = dict( + classes=( + "_background_", "apple", "banana", "book_blue", "book_white", "book_yellow", + "bowl", "can_cassis", "can_coke", "can_fanta", "can_jumbo", + "can_pepsi", "cup_black", "cup_glass", "cup_red", "fork_black", + "fork_silver", "juice_box_green", "juice_box_orange", "juice_box_pink", + "keyboard", "knife", "laptop", "lemon", "marker_blue", "marker_red", + "milk_big", "milk_small", "monitor", "mouse_black", "mouse_silver", + "orange", "peach", "pear", "pen_black", "pen_blue", "pen_red", + "plate_big", "plate_wide", "pringles_hot", "pringles_red", + "pringles_purple", "scissors_black", "scissors_silver", + "spoon_blue", "spoon_silver", "stapler" + ), + palette=[[0,0,0], [120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50], + [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255], + [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7], + [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82], + [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3], + [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255], + [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220], + [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224], + [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255], + [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7], + [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153], + [6, 51, 255], [235, 12, 255]]) + + def __init__(self, + img_suffix='.png', + seg_map_suffix='.png', + reduce_zero_label=False, + **kwargs) -> None: + super().__init__( + img_suffix=img_suffix, + seg_map_suffix=seg_map_suffix, + reduce_zero_label=reduce_zero_label, + **kwargs) + + + + + diff --git a/mmseg/datasets/hots_v1_cat.py b/mmseg/datasets/hots_v1_cat.py new file mode 100644 index 0000000000..a619a4889a --- /dev/null +++ b/mmseg/datasets/hots_v1_cat.py @@ -0,0 +1,45 @@ +from mmseg.registry import DATASETS +from .basesegdataset import BaseSegDataset + + +@DATASETS.register_module() +class HOTSCATDataset(BaseSegDataset): + + METAINFO = dict( + classes=( + '_background_', + 'apple', 'banana', 'book', 'bowl', + 'can', 'cup', 'fork', 'juice_box', + 'keyboard', 'knife', 'laptop', 'lemon', + 'marker', 'milk', 'monitor', 'mouse', + 'orange', 'peach', 'pear', 'pen', + 'plate', 'pringles', 'scissors', 'spoon', + 'stapler' + ), + palette=[ + [0, 0, 0], + [120, 120, 120], [180, 120, 120], [6, 230, 230], [120, 120, 80], + [140, 140, 140], [235, 33, 7], [8, 255, 51], [150, 100, 200], + [204, 70, 3], [0, 102, 200], [61, 230, 250], [255, 6, 51], + [11, 102, 255], [255, 9, 224], [220, 220, 220], [255, 9, 92], + [8, 255, 214], [7, 255, 224], [255, 184, 6], [10, 255, 71], + [224, 255, 8], [255, 61, 6], [0, 255, 20], [255, 5, 153], + [235, 12, 255] + ] + ) + + def __init__(self, + img_suffix='.png', + seg_map_suffix='.png', + reduce_zero_label=False, + **kwargs) -> None: + super().__init__( + img_suffix=img_suffix, + seg_map_suffix=seg_map_suffix, + reduce_zero_label=reduce_zero_label, + **kwargs) + + + + + diff --git a/mmseg/datasets/irl_vision_sim.py b/mmseg/datasets/irl_vision_sim.py new file mode 100644 index 0000000000..61a2d4ccdf --- /dev/null +++ b/mmseg/datasets/irl_vision_sim.py @@ -0,0 +1,71 @@ +from mmseg.registry import DATASETS +from .basesegdataset import BaseSegDataset + + +@DATASETS.register_module() +class IRLVisionSimDataset(BaseSegDataset): + + METAINFO = dict( + classes=( + "_background_", + "apple", "banana", "book_1", "book_10", + "book_11", "book_15", "book_20", "book_8", + "bowl_1", "bowl_2", "bowl_blue", "bowl_green", + "bowl_red", "bowl_white", "can_coke", "can_coke_zero", + "can_fanta", "can_pepsi", "can_sprite", "cap_black", + "cap_red", "cap_white", "cereal_box_1", "cereal_box_2", + "cereal_box_3", "cup", "cup_glass", "cup_glass_hex", + "cup_paper", "flashlight_black", "flashlight_blue", + "flashlight_red", + "flashlight_yellow", "fork", "juice_box_blue", + "juice_box_green", + "juice_box_orange", "juice_box_pink", "keyboard", "knife", + "laptop_mac_1", "laptop_pc_2", "lemon", "marker_black", + "marker_blue", "marker_red", "milk_box", "monitor_4", + "mouse", "mug_blue", "mug_green", "mug_red", + "mug_yellow", "orange", "peach", "pear", + "pen_black", "pen_blue", "pen_green", "pen_violet", + "plate", "plate_1", "plate_2", "pringles_green", + "pringles_hot", "pringles_orange", "scissors", + "sponge_green", + "sponge_pink", "sponge_yellow", "spoon" + + ), + palette=[ + [0, 0, 0], + [139, 152, 218], [37, 121, 67], [238, 247, 243], [22, 167, 205], + [78, 250, 236], [132, 146, 118], [133, 68, 28], [24, 180, 14], + [154, 38, 123], [153, 163, 15], [165, 45, 159], [245, 248, 135], + [0, 255, 100], [101, 140, 98], [209, 169, 129], [97, 41, 205], + [203, 172, 71], [255, 73, 168], [38, 78, 223], [23, 86, 101], + [15, 104, 200], [155, 101, 85], [171, 37, 22], [75, 119, 150], + [200, 204, 88], [243, 122, 46], [243, 165, 68], [84, 27, 26], + [177, 138, 248], [29, 30, 220], [6, 116, 22], [5, 194, 218], + [173, 227, 126], [46, 36, 59], [221, 239, 231], [193, 153, 6], + [1, 141, 223], [161, 233, 106], [0, 10, 255], [238, 1, 11], + [255, 255, 0], [135, 47, 88], [107, 225, 139], [10, 17, 218], + [100, 230, 119], [215, 252, 7], [189, 8, 9], [214, 151, 117], + [240, 238, 119], [247, 91, 58], [78, 68, 136], [193, 95, 105], + [223, 140, 16], [188, 243, 59], [247, 126, 168], [136, 52, 244], + [213, 242, 234], [253, 30, 166], [202, 242, 6], [154, 52, 185], + [83, 193, 216], [65, 107, 147], [217, 238, 57], [105, 135, 229], + [221, 252, 172], [37, 44, 104], [30, 131, 132], [72, 125, 9], + [251, 113, 172], [220, 182, 57], [216, 81, 143] + ] + ) + + def __init__(self, + img_suffix='.png', + seg_map_suffix='.png', + reduce_zero_label=False, + **kwargs) -> None: + super().__init__( + img_suffix=img_suffix, + seg_map_suffix=seg_map_suffix, + reduce_zero_label=reduce_zero_label, + **kwargs) + + + + + diff --git a/mmseg/datasets/irl_vision_sim_cat.py b/mmseg/datasets/irl_vision_sim_cat.py new file mode 100644 index 0000000000..bc1551a5af --- /dev/null +++ b/mmseg/datasets/irl_vision_sim_cat.py @@ -0,0 +1,48 @@ +from mmseg.registry import DATASETS +from .basesegdataset import BaseSegDataset + + +@DATASETS.register_module() +class IRLVisionSimCATDataset(BaseSegDataset): + + METAINFO = dict( + classes=( + '_background_', + 'apple', 'banana', 'book', 'bowl', + 'can', 'cap', 'cereal', 'cup', + 'flashlight', 'fork', 'juice_box', 'keyboard', + 'knife', 'laptop', 'lemon', 'marker', + 'milk', 'monitor', 'mouse', 'mug', + 'orange', 'peach', 'pear', 'pen', + 'plate', 'pringles', 'scissors', 'sponge', + 'spoon' + + ), + palette=[ + [0, 0, 0], + [139, 152, 218], [37, 121, 67], [238, 44, 243], [154, 38, 123], + [209, 169, 129], [23, 86, 101], [171, 37, 22], [243, 122, 46], + [29, 30, 220], [46, 36, 59], [221, 90, 231], [0, 255, 33], + [238, 1, 11], [214, 135, 52], [107, 225, 139], [10, 17, 218], + [189, 8, 9], [214, 151, 117], [240, 238, 119], [247, 91, 58], + [188, 243, 59], [247, 126, 168], [136, 52, 244], [213, 55, 234], + [83, 193, 216], [105, 135, 229], [30, 131, 132], [72, 125, 9], + [216, 81, 143] + ] + ) + + def __init__(self, + img_suffix='.png', + seg_map_suffix='.png', + reduce_zero_label=False, + **kwargs) -> None: + super().__init__( + img_suffix=img_suffix, + seg_map_suffix=seg_map_suffix, + reduce_zero_label=reduce_zero_label, + **kwargs) + + + + + diff --git a/mmseg/datasets/sodhots_c.py b/mmseg/datasets/sodhots_c.py new file mode 100644 index 0000000000..5740ce1f15 --- /dev/null +++ b/mmseg/datasets/sodhots_c.py @@ -0,0 +1,48 @@ +from mmseg.registry import DATASETS +from .basesegdataset import BaseSegDataset + + +@DATASETS.register_module() +class SODHOTSCDataset(BaseSegDataset): + + METAINFO = dict( + classes=( + '_background_', + 'apple', 'banana', 'book', 'bowl', + 'can', 'cap', 'cereal', 'cup', + 'flashlight', 'fork', 'juice_box', 'keyboard', + 'knife', 'laptop', 'lemon', 'marker', + 'milk', 'monitor', 'mouse', 'mug', + 'orange', 'peach', 'pear', 'pen', + 'plate', 'pringles', 'scissors', 'sponge', + 'spoon', 'stapler' + + ), + palette=[ + [0, 0, 0], + [139, 152, 218], [37, 121, 67], [238, 44, 243], [154, 38, 123], + [209, 169, 129], [23, 86, 101], [171, 37, 22], [243, 122, 46], + [29, 30, 220], [46, 36, 59], [221, 90, 231], [0, 255, 33], + [238, 1, 11], [214, 135, 52], [107, 225, 139], [10, 17, 218], + [189, 8, 9], [214, 151, 117], [240, 238, 119], [247, 91, 58], + [188, 243, 59], [247, 126, 168], [136, 52, 244], [213, 55, 234], + [83, 193, 216], [105, 135, 229], [30, 131, 132], [72, 125, 9], + [216, 81, 143], [244, 44, 255] + ] + ) + + def __init__(self, + img_suffix='.png', + seg_map_suffix='.png', + reduce_zero_label=False, + **kwargs) -> None: + super().__init__( + img_suffix=img_suffix, + seg_map_suffix=seg_map_suffix, + reduce_zero_label=reduce_zero_label, + **kwargs) + + + + + diff --git a/mmseg/evaluation/__init__.py b/mmseg/evaluation/__init__.py index 82b3a8d68d..f00ca8606b 100644 --- a/mmseg/evaluation/__init__.py +++ b/mmseg/evaluation/__init__.py @@ -1,4 +1,12 @@ # Copyright (c) OpenMMLab. All rights reserved. from .metrics import CityscapesMetric, DepthMetric, IoUMetric +from .metrics import ( + CustomIoUMetric, IoUMetricG, + IoUMetricFixed, CustomIoUMetricConversion +) -__all__ = ['IoUMetric', 'CityscapesMetric', 'DepthMetric'] +__all__ = [ + 'IoUMetric', 'CityscapesMetric', 'DepthMetric', + 'CustomIoUMetric', 'IoUMetricFixed', 'IoUMetricG', + 'CustomIoUMetricConversion' +] diff --git a/mmseg/evaluation/metrics/__init__.py b/mmseg/evaluation/metrics/__init__.py index 848d4713dc..52df783465 100644 --- a/mmseg/evaluation/metrics/__init__.py +++ b/mmseg/evaluation/metrics/__init__.py @@ -2,5 +2,13 @@ from .citys_metric import CityscapesMetric from .depth_metric import DepthMetric from .iou_metric import IoUMetric +from .custom_iou_metric import CustomIoUMetric +from .iou_metric_fixed import IoUMetricFixed +from .iou_georgios import IoUMetricG +from .custom_iou_metric_conversion import CustomIoUMetricConversion -__all__ = ['IoUMetric', 'CityscapesMetric', 'DepthMetric'] +__all__ = [ + 'IoUMetric', 'CityscapesMetric', 'DepthMetric', + 'CustomIoUMetric', 'IoUMetricFixed', 'IoUMetricG', + 'CustomIoUMetricConversion' +] diff --git a/mmseg/evaluation/metrics/custom_iou_metric.py b/mmseg/evaluation/metrics/custom_iou_metric.py new file mode 100644 index 0000000000..6c70811ed7 --- /dev/null +++ b/mmseg/evaluation/metrics/custom_iou_metric.py @@ -0,0 +1,301 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os.path as osp +from collections import OrderedDict +from typing import Dict, List, Optional, Sequence + +import numpy as np +import torch +from mmengine.dist import is_main_process +from mmengine.evaluator import BaseMetric +from mmengine.logging import MMLogger, print_log +from mmengine.utils import mkdir_or_exist +from PIL import Image +from prettytable import PrettyTable + +from mmseg.registry import METRICS + + +@METRICS.register_module() +class CustomIoUMetric(BaseMetric): + """IoU evaluation metric. + + Args: + ignore_index (int): Index that will be ignored in evaluation. + Default: 255. + iou_metrics (list[str] | str): Metrics to be calculated, the options + includes 'mIoU', 'mDice' and 'mFscore'. + nan_to_num (int, optional): If specified, NaN values will be replaced + by the numbers defined by the user. Default: None. + beta (int): Determines the weight of recall in the combined score. + Default: 1. + collect_device (str): Device name used for collecting results from + different ranks during distributed training. Must be 'cpu' or + 'gpu'. Defaults to 'cpu'. + output_dir (str): The directory for output prediction. Defaults to + None. + format_only (bool): Only format result for results commit without + perform evaluation. It is useful when you want to save the result + to a specific format and submit it to the test server. + Defaults to False. + prefix (str, optional): The prefix that will be added in the metric + names to disambiguate homonymous metrics of different evaluators. + If prefix is not provided in the argument, self.default_prefix + will be used instead. Defaults to None. + """ + + def __init__(self, + ignore_index: int = 255, + iou_metrics: List[str] = ['mIoU'], + nan_to_num: Optional[int] = None, + beta: int = 1, + collect_device: str = 'cpu', + output_dir: Optional[str] = None, + format_only: bool = False, + prefix: Optional[str] = None, + **kwargs) -> None: + super().__init__(collect_device=collect_device, prefix=prefix) + + self.ignore_index = ignore_index + self.metrics = iou_metrics + self.nan_to_num = nan_to_num + self.beta = beta + self.output_dir = output_dir + if self.output_dir and is_main_process(): + mkdir_or_exist(self.output_dir) + self.format_only = format_only + + def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None: + """Process one batch of data and data_samples. + + The processed results should be stored in ``self.results``, which will + be used to compute the metrics when all batches have been processed. + + Args: + data_batch (dict): A batch of data from the dataloader. + data_samples (Sequence[dict]): A batch of outputs from the model. + """ + num_classes = len(self.dataset_meta['classes']) + for data_sample in data_samples: + pred_label = data_sample['pred_sem_seg']['data'].squeeze() + # format_only always for test dataset without ground truth + if not self.format_only: + label = data_sample['gt_sem_seg']['data'].squeeze().to( + pred_label) + self.results.append( + self.intersect_and_union(pred_label, label, num_classes, + self.ignore_index)) + # format_result + if self.output_dir is not None: + basename = osp.splitext(osp.basename( + data_sample['img_path']))[0] + png_filename = osp.abspath( + osp.join(self.output_dir, f'{basename}.png')) + output_mask = pred_label.cpu().numpy() + # The index range of official ADE20k dataset is from 0 to 150. + # But the index range of output is from 0 to 149. + # That is because we set reduce_zero_label=True. + if data_sample.get('reduce_zero_label', False): + output_mask = output_mask + 1 + output = Image.fromarray(output_mask.astype(np.uint8)) + output.save(png_filename) + + def compute_metrics(self, results: list) -> Dict[str, float]: + """Compute the metrics from processed results. + + Args: + results (list): The processed results of each batch. + + Returns: + Dict[str, float]: The computed metrics. The keys are the names of + the metrics, and the values are corresponding results. The key + mainly includes aAcc, mIoU, mAcc, mDice, mFscore, mPrecision, + mRecall. + """ + logger: MMLogger = MMLogger.get_current_instance() + if self.format_only: + logger.info(f'results are saved to {osp.dirname(self.output_dir)}') + return OrderedDict() + results = tuple(zip(*results)) + assert len(results) == 4 + # total_area_intersect = sum(results[0]) + # total_area_union = sum(results[1]) + # ret_iou_mm = self.mm_iou( + # total_area_intersect=total_area_intersect, + # total_area_union=total_area_union + # ) + area_intersect = np.asarray([res.numpy() for res in results[0]]) + area_union = np.asarray([res.numpy() for res in results[1]]) + area_pred_label = np.asarray([res.numpy() for res in results[2]]) + area_label = np.asarray([res.numpy() for res in results[3]]) + + ret_metrics = self.total_area_to_metrics( + area_intersect, area_union, area_pred_label, + area_label, self.metrics, self.nan_to_num, self.beta) + + # ret_metrics["IoU_mm"] = ret_iou_mm["IoU_mm"] + + class_names = self.dataset_meta['classes'] + # summary table + ret_metrics_summary = OrderedDict({ + ret_metric: np.round(np.nanmean(ret_metric_value) * 100, 2) + for ret_metric, ret_metric_value in ret_metrics.items() + }) + metrics = dict() + for key, val in ret_metrics_summary.items(): + if key == 'aAcc': + metrics[key] = val + else: + metrics['m' + key] = val + + # each class table + ret_metrics.pop('aAcc', None) + ret_metrics_class = OrderedDict({ + ret_metric: np.round(ret_metric_value * 100, 2) + for ret_metric, ret_metric_value in ret_metrics.items() + }) + ret_metrics_class.update({'Class': class_names}) + ret_metrics_class.move_to_end('Class', last=False) + class_table_data = PrettyTable() + for key, val in ret_metrics_class.items(): + class_table_data.add_column(key, val) + + print_log('per class results:', logger) + print_log('\n' + class_table_data.get_string(), logger=logger) + + return metrics + + @staticmethod + def intersect_and_union(pred_label: torch.tensor, label: torch.tensor, + num_classes: int, ignore_index: int): + """Calculate Intersection and Union. + + Args: + pred_label (torch.tensor): Prediction segmentation map + or predict result filename. The shape is (H, W). + label (torch.tensor): Ground truth segmentation map + or label filename. The shape is (H, W). + num_classes (int): Number of categories. + ignore_index (int): Index that will be ignored in evaluation. + + Returns: + torch.Tensor: The intersection of prediction and ground truth + histogram on all classes. + torch.Tensor: The union of prediction and ground truth histogram on + all classes. + torch.Tensor: The prediction histogram on all classes. + torch.Tensor: The ground truth histogram on all classes. + """ + + mask = (label != ignore_index) + pred_label = pred_label[mask] + label = label[mask] + + intersect = pred_label[pred_label == label] + area_intersect = torch.histc( + intersect.float(), bins=(num_classes), min=0, + max=num_classes - 1).cpu() + area_pred_label = torch.histc( + pred_label.float(), bins=(num_classes), min=0, + max=num_classes - 1).cpu() + area_label = torch.histc( + label.float(), bins=(num_classes), min=0, + max=num_classes - 1).cpu() + area_union = area_pred_label + area_label - area_intersect + return area_intersect, area_union, area_pred_label, area_label + + @staticmethod + def total_area_to_metrics(area_intersect: np.ndarray, + area_union: np.ndarray, + area_pred_label: np.ndarray, + area_label: np.ndarray, + metrics: List[str] = ['mIoU'], + nan_to_num: Optional[int] = None, + beta: int = 1): + """Calculate evaluation metrics + Args: + area_intersect (np.ndarray): The intersection of prediction + and ground truth histogram on all classes. + area_union (np.ndarray): The union of prediction and ground + truth histogram on all classes. + area_pred_label (np.ndarray): The prediction histogram on + all classes. + area_label (np.ndarray): The ground truth histogram on + all classes. + metrics (List[str] | str): Metrics to be evaluated, 'mIoU' and + 'mDice'. + nan_to_num (int, optional): If specified, NaN values will be + replaced by the numbers defined by the user. Default: None. + beta (int): Determines the weight of recall in the combined score. + Default: 1. + Returns: + Dict[str, np.ndarray]: per category evaluation metrics, + shape (num_classes, ). + """ + + n_samples = area_intersect.shape[0] + n_classes = area_intersect.shape[1] + + ret_metrics = OrderedDict() + # iou per sample + for thres in np.arange(0.5, 1.0, 0.1): + class_scores = np.zeros((n_classes)) + class_counts = np.zeros((n_classes)) + for sample_idx in range(n_samples): + + inter = area_intersect[sample_idx] + union = area_union[sample_idx] + pred = area_pred_label[sample_idx] + gt = area_label[sample_idx] + iou = inter / union + class_scores += (iou > thres).astype(np.int32) + class_counts += (gt > 0).astype(np.int32) + class_scores /= class_counts + pr_key = f"Pr@{np.round(thres * 100, 2).astype(np.int32)}" + ret_metrics[pr_key] = class_scores + + + + + + + total_iou = np.sum(area_intersect, 0) / np.sum(area_union, 0) + ret_metrics["IoU"] = total_iou + + + + # ret_metrics = { + # metric: value.numpy() + # for metric, value in ret_metrics.items() + # } + # if nan_to_num is not None: + # ret_metrics = OrderedDict({ + # metric: np.nan_to_num(metric_value, nan=0.0) + # for metric, metric_value in ret_metrics.items() + # }) + # print("ret: ") + # for key, val in ret_metrics.items(): + # print(f"{key} : {val}") + # print() + + return ret_metrics + + @staticmethod + def mm_iou( + total_area_intersect: np.ndarray, + total_area_union: np.ndarray + ): + iou = total_area_intersect / total_area_union + ret_metrics = OrderedDict({'IoU_mm': iou}) + + + + ret_metrics = { + metric: value.numpy() + for metric, value in ret_metrics.items() + } + # if nan_to_num is not None: + # ret_metrics = OrderedDict({ + # metric: np.nan_to_num(metric_value, nan=nan_to_num) + # for metric, metric_value in ret_metrics.items() + # }) + return ret_metrics \ No newline at end of file diff --git a/mmseg/evaluation/metrics/custom_iou_metric_conversion.py b/mmseg/evaluation/metrics/custom_iou_metric_conversion.py new file mode 100644 index 0000000000..6699cd1807 --- /dev/null +++ b/mmseg/evaluation/metrics/custom_iou_metric_conversion.py @@ -0,0 +1,333 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os.path as osp +from collections import OrderedDict +from typing import Dict, List, Optional, Sequence + +import numpy as np +import torch +from mmengine.dist import is_main_process +from mmengine.evaluator import BaseMetric +from mmengine.logging import MMLogger, print_log +from mmengine.utils import mkdir_or_exist +from PIL import Image +from prettytable import PrettyTable + +from mmseg.registry import METRICS +from my_projects.conversion_tests.converters.dataset_converter import( + DatasetConverter +) + + +@METRICS.register_module() +class CustomIoUMetricConversion(BaseMetric): + """IoU evaluation metric. + + Args: + ignore_index (int): Index that will be ignored in evaluation. + Default: 255. + iou_metrics (list[str] | str): Metrics to be calculated, the options + includes 'mIoU', 'mDice' and 'mFscore'. + nan_to_num (int, optional): If specified, NaN values will be replaced + by the numbers defined by the user. Default: None. + beta (int): Determines the weight of recall in the combined score. + Default: 1. + collect_device (str): Device name used for collecting results from + different ranks during distributed training. Must be 'cpu' or + 'gpu'. Defaults to 'cpu'. + output_dir (str): The directory for output prediction. Defaults to + None. + format_only (bool): Only format result for results commit without + perform evaluation. It is useful when you want to save the result + to a specific format and submit it to the test server. + Defaults to False. + prefix (str, optional): The prefix that will be added in the metric + names to disambiguate homonymous metrics of different evaluators. + If prefix is not provided in the argument, self.default_prefix + will be used instead. Defaults to None. + """ + # TODO let converter determine reduce zero_label + def __init__(self, + test_dataset: str = "HOTS", + output_dataset: str = "ADE20K", + target_dataset: str = "HOTS_CAT", + ignore_index: int = 255, + iou_metrics: List[str] = ['mIoU'], + nan_to_num: Optional[int] = None, + beta: int = 1, + collect_device: str = 'cpu', + output_dir: Optional[str] = None, + format_only: bool = False, + prefix: Optional[str] = None, + **kwargs + ) -> None: + super().__init__(collect_device=collect_device, prefix=prefix) + + self.ignore_index = ignore_index + self.metrics = iou_metrics + self.nan_to_num = nan_to_num + self.beta = beta + self.output_dir = output_dir + self.data_converter = DatasetConverter( + test_dataset=test_dataset, + output_dataset=output_dataset, + target_dataset=target_dataset + ) + if self.output_dir and is_main_process(): + mkdir_or_exist(self.output_dir) + self.format_only = format_only + + def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None: + """Process one batch of data and data_samples. + + The processed results should be stored in ``self.results``, which will + be used to compute the metrics when all batches have been processed. + + Args: + data_batch (dict): A batch of data from the dataloader. + data_samples (Sequence[dict]): A batch of outputs from the model. + """ + # print(f"databatch: {data_batch}") + for data_sample in data_samples: + pred_label = data_sample['pred_sem_seg']['data'].squeeze() + # format_only always for test dataset without ground truth + # if not self.format_only: + label = data_sample['gt_sem_seg']['data'].squeeze().to( + pred_label) + pred_label, label = self.data_converter.convert_labels( + pred_label=pred_label, + gt_label=label + ) + pred_label = torch.tensor(pred_label) + label = torch.tensor(label) + self.results.append( + self.intersect_and_union( + pred_label=pred_label, + label=label, + ignore_index=self.ignore_index, + dataset_converter=self.data_converter + ) + ) + + + # format_result + if self.output_dir is not None: + basename = osp.splitext(osp.basename( + data_sample['img_path']))[0] + png_filename = osp.abspath( + osp.join(self.output_dir, f'{basename}.png')) + output_mask = pred_label.cpu().numpy() + # The index range of official ADE20k dataset is from 0 to 150. + # But the index range of output is from 0 to 149. + # That is because we set reduce_zero_label=True. + if data_sample.get('reduce_zero_label', False): + output_mask = output_mask + 1 + output = Image.fromarray(output_mask.astype(np.uint8)) + output.save(png_filename) + + def compute_metrics(self, results: list) -> Dict[str, float]: + """Compute the metrics from processed results. + + Args: + results (list): The processed results of each batch. + + Returns: + Dict[str, float]: The computed metrics. The keys are the names of + the metrics, and the values are corresponding results. The key + mainly includes aAcc, mIoU, mAcc, mDice, mFscore, mPrecision, + mRecall. + """ + logger: MMLogger = MMLogger.get_current_instance() + if self.format_only: + logger.info(f'results are saved to {osp.dirname(self.output_dir)}') + return OrderedDict() + results = tuple(zip(*results)) + assert len(results) == 4 + + area_intersect = np.asarray([np.array(res) for res in results[0]]) + area_union = np.asarray([np.array(res) for res in results[1]]) + area_pred_label = np.asarray([np.array(res) for res in results[2]]) + area_label = np.asarray([np.array(res) for res in results[3]]) + + ret_metrics = self.total_area_to_metrics( + area_intersect, area_union, area_pred_label, + area_label, self.metrics, self.nan_to_num, self.beta) + + # ret_metrics["IoU_mm"] = ret_iou_mm["IoU_mm"] + + class_names = DatasetConverter.get_class_names( + dataset_name=self.data_converter.target_dataset + ) + # summary table + ret_metrics_summary = OrderedDict({ + ret_metric: np.round(np.nanmean(ret_metric_value) * 100, 2) + for ret_metric, ret_metric_value in ret_metrics.items() + }) + metrics = dict() + for key, val in ret_metrics_summary.items(): + if key == 'aAcc': + metrics[key] = val + else: + metrics['m' + key] = val + + # each class table + ret_metrics.pop('aAcc', None) + ret_metrics_class = OrderedDict({ + ret_metric: np.round(ret_metric_value * 100, 2) + for ret_metric, ret_metric_value in ret_metrics.items() + }) + ret_metrics_class.update({'Class': class_names}) + ret_metrics_class.move_to_end('Class', last=False) + class_table_data = PrettyTable() + for key, val in ret_metrics_class.items(): + class_table_data.add_column(key, val) + + print_log('per class results:', logger) + print_log('\n' + class_table_data.get_string(), logger=logger) + + return metrics + + @staticmethod + def intersect_and_union( + pred_label: torch.tensor, + label: torch.tensor, + ignore_index: int, + dataset_converter: DatasetConverter + ): + """Calculate Intersection and Union. + + + + Returns: + torch.Tensor: The intersection of prediction and ground truth + histogram on all classes. + torch.Tensor: The union of prediction and ground truth histogram on + all classes. + torch.Tensor: The prediction histogram on all classes. + torch.Tensor: The ground truth histogram on all classes. + """ + num_classes = len( + dataset_converter.get_class_names( + dataset_name=dataset_converter.target_dataset + ) + ) + # TODO check + # pred_label, label = dataset_converter.convert_labels( + # pred_label=pred_label, + # gt_label=label + # ) + # pred_label = torch.tensor(pred_label) + # label = torch.tensor(label) + mask = (label != ignore_index) + pred_label = pred_label[mask] + label = label[mask] + + intersect = pred_label[pred_label == label] + area_intersect = torch.histc( + intersect.float(), bins=(num_classes), min=0, + max=num_classes - 1).cpu() + area_pred_label = torch.histc( + pred_label.float(), bins=(num_classes), min=0, + max=num_classes - 1).cpu() + area_label = torch.histc( + label.float(), bins=(num_classes), min=0, + max=num_classes - 1).cpu() + area_union = area_pred_label + area_label - area_intersect + return area_intersect, area_union, area_pred_label, area_label + + + @staticmethod + def total_area_to_metrics(area_intersect: np.ndarray, + area_union: np.ndarray, + area_pred_label: np.ndarray, + area_label: np.ndarray, + metrics: List[str] = ['mIoU'], + nan_to_num: Optional[int] = None, + beta: int = 1): + """Calculate evaluation metrics + Args: + area_intersect (np.ndarray): The intersection of prediction + and ground truth histogram on all classes. + area_union (np.ndarray): The union of prediction and ground + truth histogram on all classes. + area_pred_label (np.ndarray): The prediction histogram on + all classes. + area_label (np.ndarray): The ground truth histogram on + all classes. + metrics (List[str] | str): Metrics to be evaluated, 'mIoU' and + 'mDice'. + nan_to_num (int, optional): If specified, NaN values will be + replaced by the numbers defined by the user. Default: None. + beta (int): Determines the weight of recall in the combined score. + Default: 1. + Returns: + Dict[str, np.ndarray]: per category evaluation metrics, + shape (num_classes, ). + """ + + n_samples = area_intersect.shape[0] + n_classes = area_intersect.shape[1] + + ret_metrics = OrderedDict() + # iou per sample + for thres in np.arange(0.5, 1.0, 0.1): + class_scores = np.zeros((n_classes)) + class_counts = np.zeros((n_classes)) + for sample_idx in range(n_samples): + + inter = area_intersect[sample_idx] + union = area_union[sample_idx] + pred = area_pred_label[sample_idx] + gt = area_label[sample_idx] + iou = inter / union + class_scores += (iou > thres).astype(np.int32) + class_counts += (gt > 0).astype(np.int32) + class_scores /= class_counts + pr_key = f"Pr@{np.round(thres * 100, 2).astype(np.int32)}" + ret_metrics[pr_key] = class_scores + + + + + + + total_iou = np.sum(area_intersect, 0) / np.sum(area_union, 0) + ret_metrics["IoU"] = total_iou + + + + # ret_metrics = { + # metric: value.numpy() + # for metric, value in ret_metrics.items() + # } + # if nan_to_num is not None: + # ret_metrics = OrderedDict({ + # metric: np.nan_to_num(metric_value, nan=0.0) + # for metric, metric_value in ret_metrics.items() + # }) + # print("ret: ") + # for key, val in ret_metrics.items(): + # print(f"{key} : {val}") + # print() + + return ret_metrics + + @staticmethod + def mm_iou( + total_area_intersect: np.ndarray, + total_area_union: np.ndarray + ): + iou = total_area_intersect / total_area_union + ret_metrics = OrderedDict({'IoU_mm': iou}) + + + + ret_metrics = { + metric: np.asarray(value) + for metric, value in ret_metrics.items() + } + # if nan_to_num is not None: + # ret_metrics = OrderedDict({ + # metric: np.nan_to_num(metric_value, nan=nan_to_num) + # for metric, metric_value in ret_metrics.items() + # }) + return ret_metrics \ No newline at end of file diff --git a/mmseg/evaluation/metrics/iou_georgios.py b/mmseg/evaluation/metrics/iou_georgios.py new file mode 100644 index 0000000000..23d827343d --- /dev/null +++ b/mmseg/evaluation/metrics/iou_georgios.py @@ -0,0 +1,212 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os.path as osp +from collections import OrderedDict +from typing import Dict, List, Optional, Sequence + +import numpy as np +import torch +from mmengine.dist import is_main_process +from mmengine.evaluator import BaseMetric +from mmengine.logging import MMLogger, print_log +from mmengine.utils import mkdir_or_exist +from PIL import Image +from prettytable import PrettyTable + +from mmseg.registry import METRICS + + +@METRICS.register_module() +class IoUMetricG(BaseMetric): + """IoU evaluation metric. + + Args: + ignore_index (int): Index that will be ignored in evaluation. + Default: 255. + iou_metrics (list[str] | str): Metrics to be calculated, the options + includes 'mIoU', 'mDice' and 'mFscore'. + nan_to_num (int, optional): If specified, NaN values will be replaced + by the numbers defined by the user. Default: None. + beta (int): Determines the weight of recall in the combined score. + Default: 1. + collect_device (str): Device name used for collecting results from + different ranks during distributed training. Must be 'cpu' or + 'gpu'. Defaults to 'cpu'. + output_dir (str): The directory for output prediction. Defaults to + None. + format_only (bool): Only format result for results commit without + perform evaluation. It is useful when you want to save the result + to a specific format and submit it to the test server. + Defaults to False. + prefix (str, optional): The prefix that will be added in the metric + names to disambiguate homonymous metrics of different evaluators. + If prefix is not provided in the argument, self.default_prefix + will be used instead. Defaults to None. + """ + + def __init__(self, + ignore_index: int = 255, + iou_metrics: List[str] = ['mIoU'], + nan_to_num: Optional[int] = None, + beta: int = 1, + collect_device: str = 'cpu', + output_dir: Optional[str] = None, + format_only: bool = False, + prefix: Optional[str] = None, + **kwargs) -> None: + super().__init__(collect_device=collect_device, prefix=prefix) + + self.ignore_index = ignore_index + self.metrics = iou_metrics + self.nan_to_num = nan_to_num + self.beta = beta + self.output_dir = output_dir + if self.output_dir and is_main_process(): + mkdir_or_exist(self.output_dir) + self.format_only = format_only + + def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None: + """Process one batch of data and data_samples. + + The processed results should be stored in ``self.results``, which will + be used to compute the metrics when all batches have been processed. + + Args: + data_batch (dict): A batch of data from the dataloader. + data_samples (Sequence[dict]): A batch of outputs from the model. + """ + num_classes = len(self.dataset_meta['classes']) + for data_sample in data_samples: + pred_label = data_sample['pred_sem_seg']['data'].squeeze() + # format_only always for test dataset without ground truth + if not self.format_only: + label = data_sample['gt_sem_seg']['data'].squeeze().to( + pred_label) + self.results.append( + (pred_label, label) + ) + # format_result + if self.output_dir is not None: + basename = osp.splitext(osp.basename( + data_sample['img_path']))[0] + png_filename = osp.abspath( + osp.join(self.output_dir, f'{basename}.png')) + output_mask = pred_label.cpu().numpy() + # The index range of official ADE20k dataset is from 0 to 150. + # But the index range of output is from 0 to 149. + # That is because we set reduce_zero_label=True. + if data_sample.get('reduce_zero_label', False): + output_mask = output_mask + 1 + output = Image.fromarray(output_mask.astype(np.uint8)) + output.save(png_filename) + + def compute_metrics(self, results: list) -> Dict[str, float]: + """Compute the metrics from processed results. + + Args: + results (list): The processed results of each batch. + + Returns: + Dict[str, float]: The computed metrics. The keys are the names of + the metrics, and the values are corresponding results. The key + mainly includes aAcc, mIoU, mAcc, mDice, mFscore, mPrecision, + mRecall. + """ + logger: MMLogger = MMLogger.get_current_instance() + if self.format_only: + logger.info(f'results are saved to {osp.dirname(self.output_dir)}') + return OrderedDict() + # convert list of tuples to tuple of lists, e.g. + # [(A_1, B_1, C_1, D_1), ..., (A_n, B_n, C_n, D_n)] to + # ([A_1, ..., A_n], ..., [D_1, ..., D_n]) + results = tuple(zip(*results)) + assert len(results) == 2 + + # total_area_intersect = sum(results[0]) + # total_area_union = sum(results[1]) + # total_area_pred_label = sum(results[2]) + # total_area_label = sum(results[3]) + ret_metrics = self.intersect_and_union( + results[0], results[1]) + + print(ret_metrics) + + # # summary table + # ret_metrics_summary = OrderedDict({ + # ret_metric: np.round(np.nanmean(ret_metric_value) * 100, 2) + # for ret_metric, ret_metric_value in ret_metrics.items() + # }) + # metrics = dict() + # for key, val in ret_metrics_summary.items(): + # if key == 'aAcc': + # metrics[key] = val + # else: + # metrics['m' + key] = val + + # # each class table + # ret_metrics.pop('aAcc', None) + # ret_metrics_class = OrderedDict({ + # ret_metric: np.round(ret_metric_value * 100, 2) + # for ret_metric, ret_metric_value in ret_metrics.items() + # }) + # ret_metrics_class.update({'Class': class_names}) + # ret_metrics_class.move_to_end('Class', last=False) + # class_table_data = PrettyTable() + # for key, val in ret_metrics_class.items(): + # class_table_data.add_column(key, val) + + # print_log('per class results:', logger) + # print_log('\n' + class_table_data.get_string(), logger=logger) + # return ret_metrics + print_log(ret_metrics) + return ret_metrics + + @staticmethod + def intersect_and_union(pred_labels: torch.tensor, labels: torch.tensor): + """Calculate Intersection and Union. + + Args: + pred_label (torch.tensor): Prediction segmentation map + or predict result filename. The shape is (H, W). + label (torch.tensor): Ground truth segmentation map + or label filename. The shape is (H, W). + num_classes (int): Number of categories. + ignore_index (int): Index that will be ignored in evaluation. + + Returns: + torch.Tensor: The intersection of prediction and ground truth + histogram on all classes. + torch.Tensor: The union of prediction and ground truth histogram on + all classes. + torch.Tensor: The prediction histogram on all classes. + torch.Tensor: The ground truth histogram on all classes. + """ + + iou_list = [] + for pred, mask in zip(pred_labels, labels): + print(f"{pred_labels.dtype}, {labels.dtype}") + # pred: (H, W): bool, mask: (H, W): bool + # iou + inter = np.logical_and(pred, mask) + union = np.logical_or(pred, mask) + iou = np.sum(inter) / (np.sum(union) + 1e-6) + iou_list.append(iou) + iou_list = np.stack(iou_list) + iou_list = torch.from_numpy(iou_list).cpu() + prec_list = [] + for thres in torch.arange(0.5, 1.0, 0.1): + tmp = (iou_list > thres).float().mean() + prec_list.append(tmp) + iou = iou_list.mean() + prec = {} + temp = ' ' + for i, thres in enumerate(range(5, 10)): + key = 'Pr@{}'.format(thres * 10) + value = prec_list[i].item() + prec[key] = value + temp += "{}: {:.2f} ".format(key, 100. * value) + head = 'Evaluation: IoU={:.2f}'.format(100. * iou.item()) + print("ret") + print({'iou': iou.item(), **prec}) + return head + temp, {'iou': iou.item(), **prec} + + \ No newline at end of file diff --git a/mmseg/evaluation/metrics/iou_metric.py b/mmseg/evaluation/metrics/iou_metric.py index 16014c7400..7c52ddb85b 100644 --- a/mmseg/evaluation/metrics/iou_metric.py +++ b/mmseg/evaluation/metrics/iou_metric.py @@ -254,6 +254,7 @@ def f_score(precision, recall, beta=1): ret_metrics = OrderedDict({'aAcc': all_acc}) for metric in metrics: if metric == 'mIoU': + iou = total_area_intersect / total_area_union acc = total_area_intersect / total_area_label ret_metrics['IoU'] = iou diff --git a/mmseg/evaluation/metrics/iou_metric_fixed.py b/mmseg/evaluation/metrics/iou_metric_fixed.py new file mode 100644 index 0000000000..9232bae4f8 --- /dev/null +++ b/mmseg/evaluation/metrics/iou_metric_fixed.py @@ -0,0 +1,489 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import os.path as osp +from collections import OrderedDict +from typing import Dict, List, Optional, Sequence + +import numpy as np +import torch +from mmengine.dist import is_main_process +from mmengine.evaluator import BaseMetric +from mmengine.logging import MMLogger, print_log +from mmengine.utils import mkdir_or_exist +from PIL import Image +from prettytable import PrettyTable + +from mmseg.registry import METRICS + + +@METRICS.register_module() +class IoUMetricFixed(BaseMetric): + """IoU evaluation metric. + + Args: + ignore_index (int): Index that will be ignored in evaluation. + Default: 255. + iou_metrics (list[str] | str): Metrics to be calculated, the options + includes 'mIoU', 'mDice' and 'mFscore'. + nan_to_num (int, optional): If specified, NaN values will be replaced + by the numbers defined by the user. Default: None. + beta (int): Determines the weight of recall in the combined score. + Default: 1. + collect_device (str): Device name used for collecting results from + different ranks during distributed training. Must be 'cpu' or + 'gpu'. Defaults to 'cpu'. + output_dir (str): The directory for output prediction. Defaults to + None. + format_only (bool): Only format result for results commit without + perform evaluation. It is useful when you want to save the result + to a specific format and submit it to the test server. + Defaults to False. + prefix (str, optional): The prefix that will be added in the metric + names to disambiguate homonymous metrics of different evaluators. + If prefix is not provided in the argument, self.default_prefix + will be used instead. Defaults to None. + """ + + def __init__(self, + ignore_index: int = 255, + iou_metrics: List[str] = ['mIoU', 'mDice', 'mFscore', 'mAP'], + nan_to_num: Optional[int] = None, + beta: int = 1, + collect_device: str = 'cpu', + output_dir: Optional[str] = None, + format_only: bool = False, + prefix: Optional[str] = "_", + **kwargs) -> None: + super().__init__(collect_device=collect_device, prefix=prefix) + + self.ignore_index = ignore_index + self.metrics = iou_metrics + self.nan_to_num = nan_to_num + self.beta = beta + self.output_dir = output_dir + if self.output_dir and is_main_process(): + mkdir_or_exist(self.output_dir) + self.format_only = format_only + + def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None: + """Process one batch of data and data_samples. + + The processed results should be stored in ``self.results``, which will + be used to compute the metrics when all batches have been processed. + + Args: + data_batch (dict): A batch of data from the dataloader. + data_samples (Sequence[dict]): A batch of outputs from the model. + """ + num_classes = len(self.dataset_meta['classes']) + for data_sample in data_samples: + pred_label = data_sample['pred_sem_seg']['data'].squeeze() + # format_only always for test dataset without ground truth + prediction_probs = torch.zeros(pred_label.shape) + pixel_probs = torch.sigmoid(data_sample['seg_logits']['data']) + for row in range(pred_label.shape[0]): + for col in range(pred_label.shape[1]): + # prob of pred label + prediction_probs[row][col] = pixel_probs[pred_label[row][col]][row][col] + if not self.format_only: + label = data_sample['gt_sem_seg']['data'].squeeze().to( + pred_label) + self.results.append( + self.get_relevant_data( + pred_label=pred_label.cpu(), + label=label.cpu(), + prediction_probs=prediction_probs.cpu(), + num_classes=num_classes, + ignore_index=None + ) + + ) + # format_result + if self.output_dir is not None: + basename = osp.splitext(osp.basename( + data_sample['img_path']))[0] + png_filename = osp.abspath( + osp.join(self.output_dir, f'{basename}.png')) + output_mask = pred_label.cpu().numpy() + # The index range of official ADE20k dataset is from 0 to 150. + # But the index range of output is from 0 to 149. + # That is because we set reduce_zero_label=True. + if data_sample.get('reduce_zero_label', False): + output_mask = output_mask + 1 + output = Image.fromarray(output_mask.astype(np.uint8)) + output.save(png_filename) + + def compute_metrics(self, results: list) -> Dict[str, float]: + """Compute the metrics from processed results. + + Args: + results (list): The processed results of each batch. + + Returns: + Dict[str, float]: The computed metrics. The keys are the names of + the metrics, and the values are corresponding results. The key + mainly includes aAcc, mIoU, mAcc, mDice, mFscore, mPrecision, + mRecall. + """ + logger: MMLogger = MMLogger.get_current_instance() + if self.format_only: + logger.info(f'results are saved to {osp.dirname(self.output_dir)}') + return OrderedDict() + # convert list of tuples to tuple of lists, e.g. + # [(A_1, B_1, C_1, D_1), ..., (A_n, B_n, C_n, D_n)] to + # ([A_1, ..., A_n], ..., [D_1, ..., D_n]) + results = tuple(zip(*results)) + assert len(results) == 5 + + total_area_intersect = sum(results[0]) + total_area_union = sum(results[1]) + total_area_pred_label = sum(results[2]) + total_area_label = sum(results[3]) + ret_metrics = self.total_area_to_metrics( + total_area_intersect, total_area_union, total_area_pred_label, + total_area_label, self.metrics, self.nan_to_num, self.beta) + data = IoUMetricFixed.get_AP_metric( + area_intersect=results[0], + area_union=results[1], + area_pred_label=results[2], + area_label=results[3], + av_class_probs=results[4] + ) + + for key, val in data.items(): + ret_metrics[key] = val + + class_names = self.dataset_meta['classes'] + + # summary table + ret_metrics_summary = OrderedDict({ + ret_metric: np.round(np.nanmean(ret_metric_value) * 100, 2) + for ret_metric, ret_metric_value in ret_metrics.items() + }) + metrics = dict() + for key, val in ret_metrics_summary.items(): + if key == 'aAcc': + metrics[key] = val + else: + metrics['m' + key] = val + + # each class table + ret_metrics.pop('aAcc', None) + ret_metrics_class = OrderedDict({ + ret_metric: np.round(ret_metric_value * 100, 2) + for ret_metric, ret_metric_value in ret_metrics.items() + }) + ret_metrics_class.update({'Class': class_names}) + ret_metrics_class.move_to_end('Class', last=False) + class_table_data = PrettyTable() + for key, val in ret_metrics_class.items(): + class_table_data.add_column(key, val) + + print_log('per class results:', logger) + print_log('\n' + class_table_data.get_string(), logger=logger) + + return metrics + + @staticmethod + def get_relevant_data( + pred_label: torch.tensor, label: torch.tensor, + prediction_probs: torch.tensor, + num_classes: int, ignore_index: int): + """Calculate Intersection and Union. + + Args: + pred_label (torch.tensor): Prediction segmentation map + or predict result filename. The shape is (H, W). + label (torch.tensor): Ground truth segmentation map + or label filename. The shape is (H, W). + num_classes (int): Number of categories. + ignore_index (int): Index that will be ignored in evaluation. + + Returns: + torch.Tensor: The intersection of prediction and ground truth + histogram on all classes. + torch.Tensor: The union of prediction and ground truth histogram on + all classes. + torch.Tensor: The prediction histogram on all classes. + torch.Tensor: The ground truth histogram on all classes. + """ + + # mask = (label != ignore_index) + # pred_label = pred_label[mask] + # label = label[mask] + + intersect = pred_label[pred_label == label] + av_class_probs = [] + for class_idx in range(num_classes): + class_probs = prediction_probs[pred_label == class_idx] + if len(class_probs) == 0: + av_class_probs.append(0) + continue + av_class_probs.append( + sum(class_probs) / len(class_probs) + ) + av_class_probs = torch.tensor(av_class_probs) + area_intersect = torch.histc( + intersect.float(), bins=(num_classes), min=0, + max=num_classes - 1).cpu() + area_pred_label = torch.histc( + pred_label.float(), bins=(num_classes), min=0, + max=num_classes - 1).cpu() + # label = label[mask] # TODO not sure + area_label = torch.histc( + label.float(), bins=(num_classes), min=0, + max=num_classes - 1).cpu() + area_union = area_pred_label + area_label - area_intersect + return area_intersect, area_union, area_pred_label, area_label, av_class_probs + + @staticmethod + def total_area_to_metrics(total_area_intersect: np.ndarray, + total_area_union: np.ndarray, + total_area_pred_label: np.ndarray, + total_area_label: np.ndarray, + metrics: List[str] = ['mIoU', 'mDice', 'mFscore'], + nan_to_num: Optional[int] = None, + beta: int = 1): + """Calculate evaluation metrics + Args: + total_area_intersect (np.ndarray): The intersection of prediction + and ground truth histogram on all classes. + total_area_union (np.ndarray): The union of prediction and ground + truth histogram on all classes. + total_area_pred_label (np.ndarray): The prediction histogram on + all classes. + total_area_label (np.ndarray): The ground truth histogram on + all classes. + metrics (List[str] | str): Metrics to be evaluated, 'mIoU' and + 'mDice'. + nan_to_num (int, optional): If specified, NaN values will be + replaced by the numbers defined by the user. Default: None. + beta (int): Determines the weight of recall in the combined score. + Default: 1. + Returns: + Dict[str, np.ndarray]: per category evaluation metrics, + shape (num_classes, ). + """ + + def f_score(precision, recall, beta=1): + """calculate the f-score value. + + Args: + precision (float | torch.Tensor): The precision value. + recall (float | torch.Tensor): The recall value. + beta (int): Determines the weight of recall in the combined + score. Default: 1. + + Returns: + [torch.tensor]: The f-score value. + """ + score = (1 + beta**2) * (precision * recall) / ( + (beta**2 * precision) + recall) + return score + + if isinstance(metrics, str): + metrics = [metrics] + allowed_metrics = ['mIoU', 'mDice', 'mFscore', 'mAP'] + if not set(metrics).issubset(set(allowed_metrics)): + raise KeyError(f'metrics {metrics} is not supported') + + all_acc = total_area_intersect.sum() / total_area_label.sum() + ret_metrics = OrderedDict({'aAcc': all_acc}) + for metric in metrics: + if metric == 'mIoU': + iou = total_area_intersect / total_area_union + acc = total_area_intersect / total_area_label + ret_metrics['IoU'] = iou + ret_metrics['Acc'] = acc + elif metric == 'mDice': + dice = 2 * total_area_intersect / ( + total_area_pred_label + total_area_label) + acc = total_area_intersect / total_area_label + ret_metrics['Dice'] = dice + ret_metrics['Acc'] = acc + elif metric == 'mFscore': + precision = total_area_intersect / total_area_pred_label + recall = total_area_intersect / total_area_label + f_value = torch.tensor([ + f_score(x[0], x[1], beta) for x in zip(precision, recall) + ]) + ret_metrics['Fscore'] = f_value + ret_metrics['Precision'] = precision + ret_metrics['Recall'] = recall + + ret_metrics = { + metric: value.numpy() + for metric, value in ret_metrics.items() + } + if nan_to_num is not None: + ret_metrics = OrderedDict({ + metric: np.nan_to_num(metric_value, nan=nan_to_num) + for metric, metric_value in ret_metrics.items() + }) + return ret_metrics + + @staticmethod + def get_AP_metric( + area_intersect, + area_union, + area_pred_label, + area_label, + av_class_probs, + iou_thresholds = [0.25, 0.5, 0.75] + ) -> dict: + n_samples = len(area_intersect) + n_classes = len(area_intersect[0]) + data = {} + for iou_threshold in iou_thresholds: + data["ap" + str(int(iou_threshold * 100))] = [] + for iou_threshold in iou_thresholds: + + # get predicted instances and match with gt + + for class_idx in range(n_classes): + class_record = [] + total_gts_class = 0 + for sample_idx in range(n_samples): + detection_record = {} + pred_score = av_class_probs[sample_idx][class_idx] + iou = IoUMetricFixed.get_iou( + area_intersect=area_intersect[sample_idx][class_idx], + area_union=area_union[sample_idx][class_idx] + ) + if area_label[sample_idx][class_idx] > 0: + total_gts_class += 1 + detection_record["pred_score"] = pred_score + detection_record["IoU"] = iou + detection_record["confusion_label"] = IoUMetricFixed.get_confusion_label(iou=iou, iou_threshold=iou_threshold) + detection_record["area_intersect"] = area_intersect[sample_idx][class_idx] + detection_record["area_gt"] = area_label[sample_idx][class_idx] + detection_record["area_pred"] = area_pred_label[sample_idx][class_idx] + for key, val in detection_record.items(): + if key != "confusion_label" and val < 0: + print(f"negative val: {val} at key {key}") + class_record.append(detection_record) + # Data is sorted in descending order for conf thresholds + sorted_class_record = sorted( + class_record, + key=lambda d: d["pred_score"], + reverse=True + ) + # precision, recall = IoUMetricFixed.get_PR_curve_area( + # sorted_class_record=sorted_class_record + # ) + precision, recall = IoUMetricFixed.get_PR_curve_numeric( + sorted_class_record=sorted_class_record, + total_GTs_class=total_gts_class + ) + + pr_dict_list = [{"P" : p, "R" : r} for p, r in zip(precision, recall)] + sorted_PR = sorted( + pr_dict_list, + key=lambda d: d["R"], + reverse=True + ) + precision = [item["P"] for item in sorted_PR] + recall = [item["R"] for item in sorted_PR] + class_ap = IoUMetricFixed.get_average_precision( + precision=precision, + recall=recall + ) + data["ap" + str(int(iou_threshold * 100))].append(class_ap) + for iou_threshold in iou_thresholds: + data["ap" + str(int(iou_threshold * 100))] = np.asarray( + [ + float(value) for value in + data["ap" + str(int(iou_threshold * 100))] + ] + ) + return data + @staticmethod + def get_average_precision(precision: list, recall: list): + + recall.append(0) + precision.append(1) + n_points = len(precision) + return sum( + [ + (recall[k] - recall[k + 1]) * precision[k] + for k in range(n_points - 1) + ] + ) + @staticmethod + def get_PR_curve_area(sorted_class_record: list) -> tuple: + precision = [] + recall = [] + for prediction in sorted_class_record: + + precision.append( + IoUMetricFixed.get_precision_area( + area_intersect=prediction["area_intersect"], + area_pred=prediction["area_pred"] + ) + ) + recall.append( + IoUMetricFixed.get_recall_area( + area_intersection=prediction["area_intersect"], + area_gt=prediction["area_gt"] + ) + ) + return precision, recall + @staticmethod + def get_PR_curve_numeric(sorted_class_record: list, total_GTs_class: int) -> tuple: + precision = [] + recall = [] + confusion_labels = { + "TP" : 0, + "FP" : 0 + } + for prediction in sorted_class_record: + confusion_labels[prediction["confusion_label"]] += 1 + precision.append( + IoUMetricFixed.get_precision_numeric( + TP=confusion_labels["TP"], + FP=confusion_labels["FP"] + ) + ) + recall.append( + IoUMetricFixed.get_recall_numeric( + TP=confusion_labels["TP"], + total_GT=total_GTs_class + ) + ) + return precision, recall + + @staticmethod + def get_recall_numeric(TP: int, total_GT: int) -> float: + if total_GT == 0: + return 0 + return TP / total_GT + + @staticmethod + def get_recall_area(area_intersection: int, area_gt: int) -> float: + if area_gt == 0: + return 0 + return area_intersection / area_gt + + @staticmethod + def get_precision_numeric(TP: int, FP: int) -> float: + if TP + FP == 0: + return 0 + return TP / (TP + FP) + + @staticmethod + def get_precision_area(area_intersect: int, area_pred: int) -> float: + if area_pred == 0: + return 0 + return area_intersect / area_pred + + @staticmethod + def get_confusion_label(iou, iou_threshold): + if iou >= iou_threshold: + return 'TP' + else: + return 'FP' + + @staticmethod + def get_iou(area_intersect, area_union): + if area_union == 0: + return 0 + return area_intersect / area_union \ No newline at end of file diff --git a/mmseg/utils/__init__.py b/mmseg/utils/__init__.py index 0a2af58c6e..371de22bb4 100644 --- a/mmseg/utils/__init__.py +++ b/mmseg/utils/__init__.py @@ -8,7 +8,16 @@ loveda_classes, loveda_palette, potsdam_classes, potsdam_palette, stare_classes, stare_palette, synapse_classes, synapse_palette, vaihingen_classes, - vaihingen_palette, voc_classes, voc_palette) + vaihingen_palette, voc_classes, voc_palette, + hots_v1_classes, hots_v1_palette, + hots_v1_cat_classes, hots_v1_cat_palette, + irl_vision_sim_classes, irl_vision_sim_palette, + irl_vision_sim_cat_classes, + irl_vision_sim_cat_palette, + arid20cat_classes, arid20cat_palette, + arid10cat_classes, arid10cat_palette, + sodhots_c_classes, sodhots_c_palette + ) # yapf: enable from .collect_env import collect_env from .get_templates import get_predefined_templates @@ -67,4 +76,18 @@ 'MatchMasks', 'bdd100k_classes', 'bdd100k_palette', + 'hots_v1_classes', + 'hots_v1_palette', + 'hots_v1_cat_classes', + 'hots_v1_cat_palette' + 'irl_vision_sim_classes', + 'irl_vision_sim_palette', + 'irl_vision_sim_cat_classes', + 'irl_vision_sim_cat_palette', + 'arid20cat_classes', + 'arid20cat_palette', + 'arid10cat_classes', + 'arid10cat_palette', + 'sodhots_c_classes', + 'sodhots_c_palette' ] diff --git a/mmseg/utils/class_names.py b/mmseg/utils/class_names.py index 644e955966..44ab208098 100644 --- a/mmseg/utils/class_names.py +++ b/mmseg/utils/class_names.py @@ -487,10 +487,203 @@ def hsidrive_palette(): [0, 0, 255], [102, 51, 0], [255, 255, 0], [0, 207, 250], [255, 166, 0], [0, 204, 204]] +def hots_v1_classes(): + return [ + "_background_", "apple", "banana", "book_blue", "book_white", "book_yellow", + "bowl", "can_cassis", "can_coke", "can_fanta", "can_jumbo", + "can_pepsi", "cup_black", "cup_glass", "cup_red", "fork_black", + "fork_silver", "juice_box_green", "juice_box_orange", "juice_box_pink", + "keyboard", "knife", "laptop", "lemon", "marker_blue", "marker_red", + "milk_big", "milk_small", "monitor", "mouse_black", "mouse_silver", + "orange", "peach", "pear", "pen_black", "pen_blue", "pen_red", + "plate_big", "plate_wide", "pringles_hot", "pringles_red", + "pringles_purple", "scissors_black", "scissors_silver", + "spoon_blue", "spoon_silver", "stapler" + ] +def hots_v1_palette(): + return [ + [0, 0, 0], + [120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50], + [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255], + [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7], + [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82], + [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3], + [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255], + [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220], + [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224], + [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255], + [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7], + [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153], + [6, 51, 255], [235, 12, 255] + ] + +def hots_v1_cat_classes(): + return [ + '_background_', + 'apple', 'banana', 'book', 'bowl', + 'can', 'cup', 'fork', 'juice_box', + 'keyboard', 'knife', 'laptop', 'lemon', + 'marker', 'milk', 'monitor', 'mouse', + 'orange', 'peach', 'pear', 'pen', + 'plate', 'pringles', 'scissors', 'spoon', + 'stapler' + ] + +def hots_v1_cat_palette(): + return [ + [0, 0, 0], + [120, 120, 120], [180, 120, 120], [6, 230, 230], [120, 120, 80], + [140, 140, 140], [235, 33, 7], [8, 255, 51], [150, 100, 200], + [204, 70, 3], [0, 102, 200], [61, 230, 250], [255, 6, 51], + [11, 102, 255], [255, 9, 224], [220, 220, 220], [255, 9, 92], + [8, 255, 214], [7, 255, 224], [255, 184, 6], [10, 255, 71], + [224, 255, 8], [255, 61, 6], [0, 255, 20], [255, 5, 153], + [235, 12, 255] + ] +def irl_vision_sim_classes(): + return [ + "_background_", + "apple", "banana", "book_1", "book_10", + "book_11", "book_15", "book_20", "book_8", + "bowl_1", "bowl_2", "bowl_blue", "bowl_green", + "bowl_red", "bowl_white", "can_coke", "can_coke_zero", + "can_fanta", "can_pepsi", "can_sprite", "cap_black", + "cap_red", "cap_white", "cereal_box_1", "cereal_box_2", + "cereal_box_3", "cup", "cup_glass", "cup_glass_hex", + "cup_paper", "flashlight_black", "flashlight_blue", "flashlight_red", + "flashlight_yellow", "fork", "juice_box_blue", "juice_box_green", + "juice_box_orange", "juice_box_pink", "keyboard", "knife", + "laptop_mac_1", "laptop_pc_2", "lemon", "marker_black", + "marker_blue", "marker_red", "milk_box", "monitor_4", + "mouse", "mug_blue", "mug_green", "mug_red", + "mug_yellow", "orange", "peach", "pear", + "pen_black", "pen_blue", "pen_green", "pen_violet", + "plate", "plate_1", "plate_2", "pringles_green", + "pringles_hot", "pringles_orange", "scissors", "sponge_green", + "sponge_pink", "sponge_yellow", "spoon" + + ] +def irl_vision_sim_palette(): + return [ + [0, 0, 0], + [139, 152, 218], [37, 121, 67], [238, 247, 243], [22, 167, 205], + [78, 250, 236], [132, 146, 118], [133, 68, 28], [24, 180, 14], + [154, 38, 123], [153, 163, 15], [165, 45, 159], [245, 248, 135], + [0, 255, 100], [101, 140, 98], [209, 169, 129], [97, 41, 205], + [203, 172, 71], [255, 73, 168], [38, 78, 223], [23, 86, 101], + [15, 104, 200], [155, 101, 85], [255, 37, 22], [75, 119, 150], + [200, 204, 88], [243, 122, 46], [243, 165, 68], [84, 27, 26], + [177, 138, 248], [29, 30, 220], [6, 116, 22], [5, 194, 218], + [173, 227, 126], [46, 36, 59], [221, 239, 231], [193, 153, 6], + [1, 141, 223], [161, 233, 106], [0, 10, 255], [238, 1, 11], + [255, 255, 0], [255, 136, 0], [107, 225, 139], [10, 17, 218], + [100, 230, 119], [215, 252, 7], [189, 8, 9], [214, 151, 117], + [240, 238, 119], [247, 91, 58], [78, 68, 136], [193, 95, 105], + [223, 140, 16], [188, 243, 59], [247, 126, 168], [136, 52, 244], + [213, 242, 234], [253, 30, 166], [202, 242, 6], [154, 52, 185], + [83, 193, 216], [65, 107, 147], [217, 238, 57], [105, 135, 229], + [221, 252, 172], [37, 44, 104], [30, 131, 132], [72, 125, 9], + [251, 113, 172], [220, 182, 57], [216, 81, 143] + ] +def irl_vision_sim_cat_classes(): + return [ + '_background_', + 'apple', 'banana', 'book', 'bowl', + 'can', 'cap', 'cereal', 'cup', + 'flashlight', 'fork', 'juice_box', 'keyboard', + 'knife', 'laptop', 'lemon', 'marker', + 'milk', 'monitor', 'mouse', 'mug', + 'orange', 'peach', 'pear', 'pen', + 'plate', 'pringles', 'scissors', 'sponge', + 'spoon' + ] + +def irl_vision_sim_cat_palette(): + return [ + [0, 0, 0], + [139, 152, 218], [37, 121, 67], [238, 44, 243], [154, 38, 123], + [209, 169, 129], [188, 243, 59], [0,0,255], [243, 122, 46], + [29, 30, 220], [46, 36, 59], [221, 90, 231], [0, 255, 33], + [238, 1, 11], [214, 135, 52], [107, 225, 139], [10, 17, 218], + [189, 8, 9], [214, 151, 117], [240, 238, 119], [247, 91, 58], + [188, 243, 59], [247, 126, 168], [136, 52, 244], [213, 55, 234], + [83, 193, 216], [105, 135, 229], [30, 131, 132], [72, 125, 9], + [216, 81, 143] + ] +def sodhots_c_classes(): + return [ + '_background_', + 'apple', 'banana', 'book', 'bowl', + 'can', 'cap', 'cereal', 'cup', + 'flashlight', 'fork', 'juice_box', 'keyboard', + 'knife', 'laptop', 'lemon', 'marker', + 'milk', 'monitor', 'mouse', 'mug', + 'orange', 'peach', 'pear', 'pen', + 'plate', 'pringles', 'scissors', 'sponge', + 'spoon', 'stapler' + ] + +def sodhots_c_palette(): + return [ + [0, 0, 0], + [139, 152, 218], [37, 121, 67], [238, 44, 243], [154, 38, 123], + [209, 169, 129], [188, 243, 59], [0,0,255], [243, 122, 46], + [29, 30, 220], [46, 36, 59], [221, 90, 231], [0, 255, 33], + [238, 1, 11], [214, 135, 52], [107, 225, 139], [10, 17, 218], + [189, 8, 9], [214, 151, 117], [240, 238, 119], [247, 91, 58], + [188, 243, 59], [247, 126, 168], [136, 52, 244], [213, 55, 234], + [83, 193, 216], [105, 135, 229], [30, 131, 132], [72, 125, 9], + [216, 81, 143], [244, 44, 255] + ] + +def arid20cat_classes(): + return [ + "_background_", "apple", "ball", "banana", + "bell_pepper", "binder", "bowl", "cereal_box", + "coffee_mug", "flashlight", "food_bag", "food_box", + "food_can", "glue_stick", "hand_towel", "instant_noodles", + "keyboard", "kleenex", "lemon", "lime", + "marker", "orange", "peach", "pear", + "potato", "shampoo", "soda_can", "sponge", + "stapler", "tomato", "toothpaste", "_unknown_" + ] +def arid20cat_palette(): + return [ + [0, 0, 0], [69, 167, 125], [98, 36, 68], [166, 50, 141], + [127, 254, 225], [255, 243, 59], [53, 166, 152], [61, 228, 157], + [22, 187, 196], [33, 15, 250], [234, 19, 6], [95, 152, 115], + [255, 120, 0], [160, 54, 224], [252, 77, 216], [255, 0, 200], + [168, 175, 110], [190, 90, 100], [197, 39, 15], [137, 233, 180], + [233, 229, 20], [30, 241, 158], [137, 34, 172], [21, 154, 206], + [104, 190, 112], [219, 205, 202], [96, 129, 51], [32, 203, 104], + [29, 241, 69], [35, 229, 130], [124, 162, 250], [163, 156, 156] + ] +def arid10cat_classes(): + return [ + "_background_", "apple", "ball", "banana", + "bell_pepper", "binder", "bowl", "cereal_box", + "coffee_mug", "flashlight", "food_bag", "food_box", + "food_can", "glue_stick", "hand_towel", "instant_noodles", + "keyboard", "kleenex", "lemon", "lime", + "marker", "orange", "peach", "pear", + "potato", "shampoo", "soda_can", "sponge", + "stapler", "tomato", "toothpaste", "_unknown_" + ] +def arid10cat_palette(): + return [ + [0, 0, 0], [69, 167, 125], [98, 36, 68], [166, 50, 141], + [127, 254, 225], [255, 243, 59], [53, 166, 152], [61, 228, 157], + [22, 187, 196], [33, 15, 250], [234, 19, 6], [95, 152, 115], + [255, 120, 0], [160, 54, 224], [252, 77, 216], [255, 0, 200], + [168, 175, 110], [190, 90, 100], [197, 39, 15], [137, 233, 180], + [233, 229, 20], [30, 241, 158], [137, 34, 172], [21, 154, 206], + [104, 190, 112], [219, 205, 202], [96, 129, 51], [32, 203, 104], + [29, 241, 69], [35, 229, 130], [124, 162, 250], [163, 156, 156] + ] dataset_aliases = { 'cityscapes': ['cityscapes'], - 'ade': ['ade', 'ade20k'], + 'ade': ['ade', 'ade20k', 'ADE20K'], 'voc': ['voc', 'pascal_voc', 'voc12', 'voc12aug'], 'pcontext': ['pcontext', 'pascal_context', 'voc2010'], 'loveda': ['loveda'], @@ -510,7 +703,16 @@ def hsidrive_palette(): 'hsidrive': [ 'hsidrive', 'HSIDrive', 'HSI-Drive', 'hsidrive20', 'HSIDrive20', 'HSI-Drive20' - ] + ], + 'hots_v1' : ['hots_v1', 'HOTS_v1', 'HOTS'], + 'hots_v1_cat' : ["hots_v1_cat", 'HOTS_v1_cat', 'HOTS_CAT'], + 'irl_vision_sim' : ['irl_vision_sim', 'irl_vision', 'IRL_VISION'], + 'irl_vision_sim_cat' : [ + 'irl_vision_sim_cat', + 'irl_vision_cat', + 'IRL_VISION_CAT' + ], + 'arid20_cat' : ['arid20cat', 'arid20_cat', 'ARID20_CAT', 'ARID20CAT'] } diff --git a/mmseg/utils/misc.py b/mmseg/utils/misc.py index dfc469e832..ccfd87bb01 100644 --- a/mmseg/utils/misc.py +++ b/mmseg/utils/misc.py @@ -62,6 +62,7 @@ def stack_batch(inputs: List[torch.Tensor], f'but got {[tensor.shape[0] for tensor in inputs]}' # only one of size and size_divisor should be valid + # print(f"size: {size}\n size_div: {size_divisor}") assert (size is not None) ^ (size_divisor is not None), \ 'only one of size and size_divisor should be valid' diff --git a/mmseg/visualization/__init__.py b/mmseg/visualization/__init__.py index 8cbb211e52..d4c7e3e3f9 100644 --- a/mmseg/visualization/__init__.py +++ b/mmseg/visualization/__init__.py @@ -1,4 +1,9 @@ # Copyright (c) OpenMMLab. All rights reserved. from .local_visualizer import SegLocalVisualizer - -__all__ = ['SegLocalVisualizer'] +from .local_visualizer_conversion import SegLocalVisualizerConversion +from .local_visualizer_custom import SegLocalVisualizerCustom +__all__ = [ + 'SegLocalVisualizer', + 'SegLocalVisualizerConversion', + 'SegLocalVisualizerCustom' +] diff --git a/mmseg/visualization/local_visualizer.py b/mmseg/visualization/local_visualizer.py index ee3d652c7b..2a27a910eb 100644 --- a/mmseg/visualization/local_visualizer.py +++ b/mmseg/visualization/local_visualizer.py @@ -165,6 +165,7 @@ def _draw_sem_seg(self, text = classes[classes_id] (label_width, label_height), baseline = cv2.getTextSize( text, font, fontScale, thickness) + mask = cv2.rectangle(mask, loc, (loc[0] + label_width + baseline, loc[1] + label_height + baseline), diff --git a/mmseg/visualization/local_visualizer_conversion.py b/mmseg/visualization/local_visualizer_conversion.py new file mode 100644 index 0000000000..9cd2b481c6 --- /dev/null +++ b/mmseg/visualization/local_visualizer_conversion.py @@ -0,0 +1,426 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from typing import Dict, List, Optional + +import cv2 +import mmcv +import numpy as np +import torch +from mmengine.dist import master_only +from mmengine.structures import PixelData +from mmengine.visualization import Visualizer + +from mmseg.registry import VISUALIZERS +from mmseg.structures import SegDataSample +from mmseg.utils import get_classes, get_palette +from my_projects.conversion_tests.converters.dataset_converter import( + DatasetConverter +) +from copy import deepcopy +@VISUALIZERS.register_module() +class SegLocalVisualizerConversion(Visualizer): + """Local Visualizer. + + Args: + name (str): Name of the instance. Defaults to 'visualizer'. + image (np.ndarray, optional): the origin image to draw. The format + should be RGB. Defaults to None. + vis_backends (list, optional): Visual backend config list. + Defaults to None. + save_dir (str, optional): Save file dir for all storage backends. + If it is None, the backend storage will not save any data. + classes (list, optional): Input classes for result rendering, as the + prediction of segmentation model is a segment map with label + indices, `classes` is a list which includes items responding to the + label indices. If classes is not defined, visualizer will take + `cityscapes` classes by default. Defaults to None. + palette (list, optional): Input palette for result rendering, which is + a list of color palette responding to the classes. Defaults to None. + dataset_name (str, optional): `Dataset name or alias `_ + visulizer will use the meta information of the dataset i.e. classes + and palette, but the `classes` and `palette` have higher priority. + Defaults to None. + alpha (int, float): The transparency of segmentation mask. + Defaults to 0.8. + + Examples: + >>> import numpy as np + >>> import torch + >>> from mmengine.structures import PixelData + >>> from mmseg.structures import SegDataSample + >>> from mmseg.visualization import SegLocalVisualizer + + >>> seg_local_visualizer = SegLocalVisualizer() + >>> image = np.random.randint(0, 256, + ... size=(10, 12, 3)).astype('uint8') + >>> gt_sem_seg_data = dict(data=torch.randint(0, 2, (1, 10, 12))) + >>> gt_sem_seg = PixelData(**gt_sem_seg_data) + >>> gt_seg_data_sample = SegDataSample() + >>> gt_seg_data_sample.gt_sem_seg = gt_sem_seg + >>> seg_local_visualizer.dataset_meta = dict( + >>> classes=('background', 'foreground'), + >>> palette=[[120, 120, 120], [6, 230, 230]]) + >>> seg_local_visualizer.add_datasample('visualizer_example', + ... image, gt_seg_data_sample) + >>> seg_local_visualizer.add_datasample( + ... 'visualizer_example', image, + ... gt_seg_data_sample, show=True) + """ # noqa + + def __init__( + self, + name: str = 'visualizer', + test_dataset: str = "HOTS", + output_dataset: str = "ADE20K", + target_dataset: str = "HOTS_CAT", + unknown_idx: int = 255, + image: Optional[np.ndarray] = None, + vis_backends: Optional[Dict] = None, + save_dir: Optional[str] = None, + alpha: float = 0.8, + **kwargs + ): + super().__init__(name, image, vis_backends, save_dir, **kwargs) + self.alpha: float = alpha + self.dataset_converter = DatasetConverter( + test_dataset=test_dataset, + output_dataset=output_dataset, + target_dataset=target_dataset, + unknown_idx=unknown_idx + ) + + self.set_dataset_meta( + classes=DatasetConverter.get_class_names(target_dataset), + palette=DatasetConverter.get_palette(target_dataset), + dataset_name=target_dataset + ) + + + def _get_center_loc(self, mask: np.ndarray) -> np.ndarray: + """Get semantic seg center coordinate. + + Args: + mask: np.ndarray: get from sem_seg + """ + loc = np.argwhere(mask == 1) + + loc_sort = np.array( + sorted(loc.tolist(), key=lambda row: (row[0], row[1]))) + y_list = loc_sort[:, 0] + unique, indices, counts = np.unique( + y_list, return_index=True, return_counts=True) + y_loc = unique[counts.argmax()] + y_most_freq_loc = loc[loc_sort[:, 0] == y_loc] + center_num = len(y_most_freq_loc) // 2 + x = y_most_freq_loc[center_num][1] + y = y_most_freq_loc[center_num][0] + return np.array([x, y]) + + + def _draw_sem_seg(self, + image: np.ndarray, + sem_seg: PixelData, + classes: Optional[List], + palette: Optional[List], + with_labels: Optional[bool] = True) -> np.ndarray: + """Draw semantic seg of GT or prediction. + + Args: + image (np.ndarray): The image to draw. + sem_seg (:obj:`PixelData`): Data structure for pixel-level + annotations or predictions. + classes (list, optional): Input classes for result rendering, as + the prediction of segmentation model is a segment map with + label indices, `classes` is a list which includes items + responding to the label indices. If classes is not defined, + visualizer will take `cityscapes` classes by default. + Defaults to None. + palette (list, optional): Input palette for result rendering, which + is a list of color palette responding to the classes. + Defaults to None. + with_labels(bool, optional): Add semantic labels in visualization + result, Default to True. + + Returns: + np.ndarray: the drawn image which channel is RGB. + """ + num_classes = len(classes) + + sem_seg = sem_seg.cpu().data + unknown_idx = self.dataset_converter.unknown_idx + ids = np.unique(sem_seg)[::-1] + legal_indices = ids < num_classes + # legal_indices[ids == unknown_idx] = True + ids_ = ids[legal_indices] + labels = np.array(ids_, dtype=np.int64) + + + colors = [ + palette[label] for label in labels + if label != unknown_idx + ] + if unknown_idx in ids: + labels = np.append(labels, [unknown_idx]) + colors.append(palette[0]) + + mask = np.zeros_like(image, dtype=np.uint8) + for label, color in zip(labels, colors): + mask[sem_seg[0] == label, :] = color + + if with_labels: + font = cv2.FONT_HERSHEY_SIMPLEX + # (0,1] to change the size of the text relative to the image + scale = 0.05 + fontScale = min(image.shape[0], image.shape[1]) / (25 / scale) + fontColor = (255, 255, 255) + if image.shape[0] < 300 or image.shape[1] < 300: + thickness = 1 + rectangleThickness = 1 + else: + thickness = 2 + rectangleThickness = 2 + lineType = 2 + + if isinstance(sem_seg[0], torch.Tensor): + masks = sem_seg[0].numpy() == labels[:, None, None] + else: + masks = sem_seg[0] == labels[:, None, None] + masks = masks.astype(np.uint8) + for mask_num in range(len(labels)): + classes_id = labels[mask_num] + classes_color = colors[mask_num] + loc = self._get_center_loc(masks[mask_num]) + text = "_unknown_" + if classes_id != unknown_idx: + text = classes[classes_id] + (label_width, label_height), baseline = cv2.getTextSize( + text, font, fontScale, thickness) + + mask = cv2.rectangle(mask, loc, + (loc[0] + label_width + baseline, + loc[1] + label_height + baseline), + classes_color, -1) + mask = cv2.rectangle(mask, loc, + (loc[0] + label_width + baseline, + loc[1] + label_height + baseline), + (0, 0, 0), rectangleThickness) + mask = cv2.putText(mask, text, (loc[0], loc[1] + label_height), + font, fontScale, fontColor, thickness, + lineType) + color_seg = (image * (1 - self.alpha) + mask * self.alpha).astype( + np.uint8) + self.set_image(color_seg) + return color_seg + + def convert_sem_seg_data( + self, + data_sample: SegDataSample + ): + converted_sample = SegDataSample(metainfo=data_sample.metainfo) + + if 'gt_sem_seg' in data_sample: + gt_data = data_sample.gt_sem_seg.cpu().data + gt_shape = gt_data.shape + gt_converted = self.dataset_converter.convert_gt_label( + label=np.asarray(gt_data.flatten()) + ) + gt_data = torch.tensor(gt_converted.reshape(gt_shape)) + gt_seg = PixelData(metainfo=data_sample.gt_sem_seg.metainfo) + gt_seg.data = gt_data + converted_sample.gt_sem_seg = gt_seg + + if 'pred_sem_seg' in data_sample: + pred_data = data_sample.pred_sem_seg.cpu().data + pred_shape = pred_data.shape + pred_converted = self.dataset_converter.convert_pred_label( + pred_label=np.asarray(pred_data.flatten()), + gt_label_converted=gt_converted + ) + pred_data = torch.tensor(pred_converted.reshape(pred_shape)) + pred_seg = PixelData(metainfo=data_sample.pred_sem_seg.metainfo) + pred_seg.data = pred_data + converted_sample.pred_sem_seg = pred_seg + return converted_sample + + + + + def _draw_depth_map(self, image: np.ndarray, + depth_map: PixelData) -> np.ndarray: + """Draws a depth map on a given image. + + This function takes an image and a depth map as input, + renders the depth map, and concatenates it with the original image. + Finally, it updates the internal image state of the visualizer with + the concatenated result. + + Args: + image (np.ndarray): The original image where the depth map will + be drawn. The array should be in the format HxWx3 where H is + the height, W is the width. + + depth_map (PixelData): Depth map to be drawn. The depth map + should be in the form of a PixelData object. It will be + converted to a torch tensor if it is a numpy array. + + Returns: + np.ndarray: The concatenated image with the depth map drawn. + + Example: + >>> depth_map_data = PixelData(data=torch.rand(1, 10, 10)) + >>> image = np.random.randint(0, 256, + >>> size=(10, 10, 3)).astype('uint8') + >>> visualizer = SegLocalVisualizer() + >>> visualizer._draw_depth_map(image, depth_map_data) + """ + depth_map = depth_map.cpu().data + if isinstance(depth_map, np.ndarray): + depth_map = torch.from_numpy(depth_map) + if depth_map.ndim == 2: + depth_map = depth_map[None] + + depth_map = self.draw_featmap(depth_map, resize_shape=image.shape[:2]) + out_image = np.concatenate((image, depth_map), axis=0) + self.set_image(out_image) + return out_image + + def set_dataset_meta(self, + classes: Optional[List] = None, + palette: Optional[List] = None, + dataset_name: Optional[str] = None) -> None: + """Set meta information to visualizer. + + Args: + classes (list, optional): Input classes for result rendering, as + the prediction of segmentation model is a segment map with + label indices, `classes` is a list which includes items + responding to the label indices. If classes is not defined, + visualizer will take `cityscapes` classes by default. + Defaults to None. + palette (list, optional): Input palette for result rendering, which + is a list of color palette responding to the classes. + Defaults to None. + dataset_name (str, optional): `Dataset name or alias `_ + visulizer will use the meta information of the dataset i.e. + classes and palette, but the `classes` and `palette` have + higher priority. Defaults to None. + """ # noqa + # Set default value. When calling + # `SegLocalVisualizer().dataset_meta=xxx`, + # it will override the default value. + if dataset_name is None: + dataset_name = 'cityscapes' + classes = classes if classes else get_classes(dataset_name) + palette = palette if palette else get_palette(dataset_name) + assert len(classes) == len( + palette), 'The length of classes should be equal to palette' + self.dataset_meta: dict = {'classes': classes, 'palette': palette} + + @master_only + def add_datasample( + self, + name: str, + image: np.ndarray, + data_sample: Optional[SegDataSample] = None, + draw_gt: bool = True, + draw_pred: bool = True, + show: bool = False, + wait_time: float = 0, + # TODO: Supported in mmengine's Viusalizer. + out_file: Optional[str] = None, + step: int = 0, + with_labels: Optional[bool] = True) -> None: + """Draw datasample and save to all backends. + + - If GT and prediction are plotted at the same time, they are + displayed in a stitched image where the left image is the + ground truth and the right image is the prediction. + - If ``show`` is True, all storage backends are ignored, and + the images will be displayed in a local window. + - If ``out_file`` is specified, the drawn image will be + saved to ``out_file``. it is usually used when the display + is not available. + + Args: + name (str): The image identifier. + image (np.ndarray): The image to draw. + gt_sample (:obj:`SegDataSample`, optional): GT SegDataSample. + Defaults to None. + pred_sample (:obj:`SegDataSample`, optional): Prediction + SegDataSample. Defaults to None. + draw_gt (bool): Whether to draw GT SegDataSample. Default to True. + draw_pred (bool): Whether to draw Prediction SegDataSample. + Defaults to True. + show (bool): Whether to display the drawn image. Default to False. + wait_time (float): The interval of show (s). Defaults to 0. + out_file (str): Path to output file. Defaults to None. + step (int): Global step value to record. Defaults to 0. + with_labels(bool, optional): Add semantic labels in visualization + result, Defaults to True. + """ + target_dataset = self.dataset_converter.target_dataset + classes=DatasetConverter.get_class_names(target_dataset) + palette=DatasetConverter.get_palette(target_dataset) + # print(f"dataset_meta: {self.dataset_meta}") + gt_img_data = None + pred_img_data = None + # print(f"\nvis: datasample pre converted:{data_sample}") + data_sample = self.convert_sem_seg_data(data_sample=data_sample) + # print(f"\nvis: datasample converted:\n{data_sample}") + if draw_gt and data_sample is not None: + if 'gt_sem_seg' in data_sample: + assert classes is not None, 'class information is ' \ + 'not provided when ' \ + 'visualizing semantic ' \ + 'segmentation results.' + + gt_img_data = self._draw_sem_seg( + image=image, + sem_seg=data_sample.gt_sem_seg, + classes=classes, + palette=palette, + with_labels=True + ) + + + if 'gt_depth_map' in data_sample: + gt_img_data = gt_img_data if gt_img_data is not None else image + gt_img_data = self._draw_depth_map(gt_img_data, + data_sample.gt_depth_map) + + if draw_pred and data_sample is not None: + + if 'pred_sem_seg' in data_sample: + + assert classes is not None, 'class information is ' \ + 'not provided when ' \ + 'visualizing semantic ' \ + 'segmentation results.' + + pred_img_data = self._draw_sem_seg( + image=image, + sem_seg=data_sample.pred_sem_seg, + classes=classes, + palette=palette, + with_labels=True + ) + + if 'pred_depth_map' in data_sample: + pred_img_data = pred_img_data if pred_img_data is not None \ + else image + pred_img_data = self._draw_depth_map( + pred_img_data, data_sample.pred_depth_map) + + if gt_img_data is not None and pred_img_data is not None: + drawn_img = np.concatenate((gt_img_data, pred_img_data), axis=1) + elif gt_img_data is not None: + drawn_img = gt_img_data + else: + drawn_img = pred_img_data + + if show: + self.show(drawn_img, win_name=name, wait_time=wait_time) + + if out_file is not None: + mmcv.imwrite(mmcv.rgb2bgr(drawn_img), out_file) + else: + self.add_image(name, drawn_img, step) diff --git a/mmseg/visualization/local_visualizer_custom.py b/mmseg/visualization/local_visualizer_custom.py new file mode 100644 index 0000000000..d7a8c07aef --- /dev/null +++ b/mmseg/visualization/local_visualizer_custom.py @@ -0,0 +1,371 @@ +# Copyright (c) OpenMMLab. All rights reserved. +from typing import Dict, List, Optional + +import cv2 +import mmcv +import numpy as np +import torch +from mmengine.dist import master_only +from mmengine.structures import PixelData +from mmengine.visualization import Visualizer + +from mmseg.registry import VISUALIZERS +from mmseg.structures import SegDataSample +from mmseg.utils import get_classes, get_palette + + +@VISUALIZERS.register_module() +class SegLocalVisualizerCustom(Visualizer): + """Local Visualizer. + + Args: + name (str): Name of the instance. Defaults to 'visualizer'. + image (np.ndarray, optional): the origin image to draw. The format + should be RGB. Defaults to None. + vis_backends (list, optional): Visual backend config list. + Defaults to None. + save_dir (str, optional): Save file dir for all storage backends. + If it is None, the backend storage will not save any data. + classes (list, optional): Input classes for result rendering, as the + prediction of segmentation model is a segment map with label + indices, `classes` is a list which includes items responding to the + label indices. If classes is not defined, visualizer will take + `cityscapes` classes by default. Defaults to None. + palette (list, optional): Input palette for result rendering, which is + a list of color palette responding to the classes. Defaults to None. + dataset_name (str, optional): `Dataset name or alias `_ + visulizer will use the meta information of the dataset i.e. classes + and palette, but the `classes` and `palette` have higher priority. + Defaults to None. + alpha (int, float): The transparency of segmentation mask. + Defaults to 0.8. + + Examples: + >>> import numpy as np + >>> import torch + >>> from mmengine.structures import PixelData + >>> from mmseg.structures import SegDataSample + >>> from mmseg.visualization import SegLocalVisualizer + + >>> seg_local_visualizer = SegLocalVisualizer() + >>> image = np.random.randint(0, 256, + ... size=(10, 12, 3)).astype('uint8') + >>> gt_sem_seg_data = dict(data=torch.randint(0, 2, (1, 10, 12))) + >>> gt_sem_seg = PixelData(**gt_sem_seg_data) + >>> gt_seg_data_sample = SegDataSample() + >>> gt_seg_data_sample.gt_sem_seg = gt_sem_seg + >>> seg_local_visualizer.dataset_meta = dict( + >>> classes=('background', 'foreground'), + >>> palette=[[120, 120, 120], [6, 230, 230]]) + >>> seg_local_visualizer.add_datasample('visualizer_example', + ... image, gt_seg_data_sample) + >>> seg_local_visualizer.add_datasample( + ... 'visualizer_example', image, + ... gt_seg_data_sample, show=True) + """ # noqa + + def __init__(self, + name: str = 'visualizer', + image: Optional[np.ndarray] = None, + vis_backends: Optional[Dict] = None, + save_dir: Optional[str] = None, + classes: Optional[List] = None, + palette: Optional[List] = None, + dataset_name: Optional[str] = None, + alpha: float = 0.8, + **kwargs): + super().__init__(name, image, vis_backends, save_dir, **kwargs) + self.alpha: float = alpha + self.set_dataset_meta(palette, classes, dataset_name) + + def _get_center_loc(self, mask: np.ndarray) -> np.ndarray: + """Get semantic seg center coordinate. + + Args: + mask: np.ndarray: get from sem_seg + """ + loc = np.argwhere(mask == 1) + + loc_sort = np.array( + sorted(loc.tolist(), key=lambda row: (row[0], row[1]))) + y_list = loc_sort[:, 0] + unique, indices, counts = np.unique( + y_list, return_index=True, return_counts=True) + y_loc = unique[counts.argmax()] + y_most_freq_loc = loc[loc_sort[:, 0] == y_loc] + center_num = len(y_most_freq_loc) // 2 + x = y_most_freq_loc[center_num][1] + y = y_most_freq_loc[center_num][0] + return np.array([x, y]) + + def _draw_sem_seg(self, + image: np.ndarray, + sem_seg: PixelData, + classes: Optional[List], + palette: Optional[List], + with_labels: Optional[bool] = True, + ignore_idx: Optional[int] = 255, + label_scale = 0.05, + ignore_background = True + ) -> np.ndarray: + """Draw semantic seg of GT or prediction. + + Args: + image (np.ndarray): The image to draw. + sem_seg (:obj:`PixelData`): Data structure for pixel-level + annotations or predictions. + classes (list, optional): Input classes for result rendering, as + the prediction of segmentation model is a segment map with + label indices, `classes` is a list which includes items + responding to the label indices. If classes is not defined, + visualizer will take `cityscapes` classes by default. + Defaults to None. + palette (list, optional): Input palette for result rendering, which + is a list of color palette responding to the classes. + Defaults to None. + with_labels(bool, optional): Add semantic labels in visualization + result, Default to True. + + Returns: + np.ndarray: the drawn image which channel is RGB. + """ + num_classes = len(classes) + + sem_seg = sem_seg.cpu().data + ids = np.unique(sem_seg)[::-1] + legal_indices = ids < num_classes + ids = ids[legal_indices] + labels = np.array(ids, dtype=np.int64) + + colors = [palette[label] for label in labels] + + mask = np.zeros_like(image, dtype=np.uint8) + for label, color in zip(labels, colors): + if ignore_background and label == ignore_idx: + mask[sem_seg[0] == label, :] = image[sem_seg[0] == label, :] + continue + mask[sem_seg[0] == label, :] = color + + if with_labels: + font = cv2.FONT_HERSHEY_SIMPLEX + # (0,1] to change the size of the text relative to the image + + fontScale = min(image.shape[0], image.shape[1]) / (25 / label_scale) + fontColor = (255, 255, 255) + if image.shape[0] < 300 or image.shape[1] < 300: + thickness = 1 + rectangleThickness = 1 + else: + thickness = 2 + rectangleThickness = 2 + lineType = 2 + + if isinstance(sem_seg[0], torch.Tensor): + masks = sem_seg[0].numpy() == labels[:, None, None] + else: + masks = sem_seg[0] == labels[:, None, None] + masks = masks.astype(np.uint8) + for mask_num in range(len(labels)): + classes_id = labels[mask_num] + classes_color = colors[mask_num] + loc = self._get_center_loc(masks[mask_num]) + text = classes[classes_id] + if ignore_background and text == "_background_": + continue + (label_width, label_height), baseline = cv2.getTextSize( + text, font, fontScale, thickness) + + mask = cv2.rectangle(mask, loc, + (loc[0] + label_width + baseline, + loc[1] + label_height + baseline), + (0, 0, 0), -1) + # mask = cv2.rectangle(mask, loc, + # (loc[0] + label_width + baseline, + # loc[1] + label_height + baseline), + # (0, 0, 0), rectangleThickness) + + mask = cv2.putText(mask, text, (loc[0], loc[1] + label_height), + font, fontScale, fontColor, thickness, + lineType) + + + color_seg = (image * (1 - self.alpha) + mask * self.alpha).astype( + np.uint8) + # if ignore_background: + # bg_mask = np.zeros_like(image, dtype=np.uint8) + # bg_alpha = 0.8 + # bg_mask[sem_seg[0] == ignore_idx, :] = palette[ignore_idx] + # color_seg = ( + # image * (1 - self.alpha) + bg_mask * bg_alpha + mask * self.alpha + # ).astype( + # np.uint8) + self.set_image(color_seg) + + return color_seg + + def _draw_depth_map(self, image: np.ndarray, + depth_map: PixelData) -> np.ndarray: + """Draws a depth map on a given image. + + This function takes an image and a depth map as input, + renders the depth map, and concatenates it with the original image. + Finally, it updates the internal image state of the visualizer with + the concatenated result. + + Args: + image (np.ndarray): The original image where the depth map will + be drawn. The array should be in the format HxWx3 where H is + the height, W is the width. + + depth_map (PixelData): Depth map to be drawn. The depth map + should be in the form of a PixelData object. It will be + converted to a torch tensor if it is a numpy array. + + Returns: + np.ndarray: The concatenated image with the depth map drawn. + + Example: + >>> depth_map_data = PixelData(data=torch.rand(1, 10, 10)) + >>> image = np.random.randint(0, 256, + >>> size=(10, 10, 3)).astype('uint8') + >>> visualizer = SegLocalVisualizer() + >>> visualizer._draw_depth_map(image, depth_map_data) + """ + depth_map = depth_map.cpu().data + if isinstance(depth_map, np.ndarray): + depth_map = torch.from_numpy(depth_map) + if depth_map.ndim == 2: + depth_map = depth_map[None] + + depth_map = self.draw_featmap(depth_map, resize_shape=image.shape[:2]) + out_image = np.concatenate((image, depth_map), axis=0) + self.set_image(out_image) + return out_image + + def set_dataset_meta(self, + classes: Optional[List] = None, + palette: Optional[List] = None, + dataset_name: Optional[str] = None) -> None: + """Set meta information to visualizer. + + Args: + classes (list, optional): Input classes for result rendering, as + the prediction of segmentation model is a segment map with + label indices, `classes` is a list which includes items + responding to the label indices. If classes is not defined, + visualizer will take `cityscapes` classes by default. + Defaults to None. + palette (list, optional): Input palette for result rendering, which + is a list of color palette responding to the classes. + Defaults to None. + dataset_name (str, optional): `Dataset name or alias `_ + visulizer will use the meta information of the dataset i.e. + classes and palette, but the `classes` and `palette` have + higher priority. Defaults to None. + """ # noqa + # Set default value. When calling + # `SegLocalVisualizer().dataset_meta=xxx`, + # it will override the default value. + if dataset_name is None: + dataset_name = 'cityscapes' + classes = classes if classes else get_classes(dataset_name) + palette = palette if palette else get_palette(dataset_name) + assert len(classes) == len( + palette), 'The length of classes should be equal to palette' + self.dataset_meta: dict = {'classes': classes, 'palette': palette} + + @master_only + def add_datasample( + self, + name: str, + image: np.ndarray, + data_sample: Optional[SegDataSample] = None, + draw_gt: bool = True, + draw_pred: bool = True, + show: bool = False, + wait_time: float = 0, + # TODO: Supported in mmengine's Viusalizer. + out_file: Optional[str] = None, + step: int = 0, + with_labels: Optional[bool] = True) -> None: + """Draw datasample and save to all backends. + + - If GT and prediction are plotted at the same time, they are + displayed in a stitched image where the left image is the + ground truth and the right image is the prediction. + - If ``show`` is True, all storage backends are ignored, and + the images will be displayed in a local window. + - If ``out_file`` is specified, the drawn image will be + saved to ``out_file``. it is usually used when the display + is not available. + + Args: + name (str): The image identifier. + image (np.ndarray): The image to draw. + gt_sample (:obj:`SegDataSample`, optional): GT SegDataSample. + Defaults to None. + pred_sample (:obj:`SegDataSample`, optional): Prediction + SegDataSample. Defaults to None. + draw_gt (bool): Whether to draw GT SegDataSample. Default to True. + draw_pred (bool): Whether to draw Prediction SegDataSample. + Defaults to True. + show (bool): Whether to display the drawn image. Default to False. + wait_time (float): The interval of show (s). Defaults to 0. + out_file (str): Path to output file. Defaults to None. + step (int): Global step value to record. Defaults to 0. + with_labels(bool, optional): Add semantic labels in visualization + result, Defaults to True. + """ + classes = self.dataset_meta.get('classes', None) + palette = self.dataset_meta.get('palette', None) + + gt_img_data = None + pred_img_data = None + + if draw_gt and data_sample is not None: + if 'gt_sem_seg' in data_sample: + assert classes is not None, 'class information is ' \ + 'not provided when ' \ + 'visualizing semantic ' \ + 'segmentation results.' + gt_img_data = self._draw_sem_seg(image, data_sample.gt_sem_seg, + classes, palette, with_labels) + + if 'gt_depth_map' in data_sample: + gt_img_data = gt_img_data if gt_img_data is not None else image + gt_img_data = self._draw_depth_map(gt_img_data, + data_sample.gt_depth_map) + + if draw_pred and data_sample is not None: + + if 'pred_sem_seg' in data_sample: + + assert classes is not None, 'class information is ' \ + 'not provided when ' \ + 'visualizing semantic ' \ + 'segmentation results.' + pred_img_data = self._draw_sem_seg(image, + data_sample.pred_sem_seg, + classes, palette, + with_labels) + + if 'pred_depth_map' in data_sample: + pred_img_data = pred_img_data if pred_img_data is not None \ + else image + pred_img_data = self._draw_depth_map( + pred_img_data, data_sample.pred_depth_map) + + if gt_img_data is not None and pred_img_data is not None: + drawn_img = np.concatenate((gt_img_data, pred_img_data), axis=1) + elif gt_img_data is not None: + drawn_img = gt_img_data + else: + drawn_img = pred_img_data + + if show: + self.show(drawn_img, win_name=name, wait_time=wait_time) + + if out_file is not None: + mmcv.imwrite(mmcv.rgb2bgr(drawn_img), out_file) + else: + self.add_image(name, drawn_img, step) diff --git a/my_projects/__init__.py b/my_projects/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/my_projects/ablation_tests/scripts/organise_scene_results.py b/my_projects/ablation_tests/scripts/organise_scene_results.py new file mode 100644 index 0000000000..3bfc1ed69f --- /dev/null +++ b/my_projects/ablation_tests/scripts/organise_scene_results.py @@ -0,0 +1,104 @@ +import os +import argparse +import json + +def find_json_file(scene_dir_path): + for root, dirs, files in os.walk(os.path.join(scene_dir_path)): + json_file = [file for file in files if ".json" in file] + if json_file: + return os.path.join(root, json_file[0]) + return None + +def fix_keys_scene_dict( + scene_dict, + keys_of_interest = [ + "mPr@50", "mPr@60", "mPr@70", + "mPr@80", "mPr@90", "mIoU" + ] +): + fixed_dict = {} + for scene_id, scene_data in scene_dict.items(): + fixed_dict[scene_id] = {} + for metric_key, metric_val in scene_data.items(): + fixed_key = metric_key.split(".")[0] + if fixed_key in keys_of_interest: + fixed_dict[scene_id][fixed_key] = metric_val + return fixed_dict + + +def collect_json_files(model_name, args): + scene_dict = {} + model_path = os.path.join( + args.results_path, + model_name + ) + for scene_dir in os.listdir(model_path): + json_file_path = find_json_file( + scene_dir_path=os.path.join( + model_path, + scene_dir + ) + ) + if json_file_path is None: + continue + with open(json_file_path, 'r') as json_file: + scene_dict[scene_dir] = json.load(json_file) + if args.include_av: + with open(args.average_test_results_path, 'r') as json_file: + model_key = model_name.split("_")[0] + scene_dict["overall"] = json.load(json_file)[model_key] + scene_dict = fix_keys_scene_dict(scene_dict=scene_dict) + if args.save_intermediate: + scene_dump_path = os.path.join(model_path, "scene_data.json") + with open(scene_dump_path, 'w') as dump_file: + json.dump(scene_dict, dump_file, indent=4) + return scene_dict + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + 'results_path', + type=str + ) + parser.add_argument( + '--save_intermediate', + '-si', + action='store_true' + ) + parser.add_argument( + '--average_test_results_path', + '-av', + type=str, + default=None + ) + parser.add_argument( + '--include_av', + '-incl', + action='store_true' + ) + + args = parser.parse_args() + if args.include_av and args.average_test_results_path is None: + args.average_test_results_path = "my_projects/test_results/arid20_cat/data/arid20_cat_performance_results.json" + return args +def main(): + + args = parse_args() + data_dict = {} + for model_name in os.listdir(args.results_path): + if model_name == "data": + continue + model_path = os.path.join(args.results_path, model_name) + if not os.path.isdir(model_path): + continue + scene_dict = collect_json_files(model_name, args) + data_dict[model_name.split("_")[0]] = scene_dict + results_data_file_path = os.path.join( + args.results_path, + "scene_results.json" + ) + with open(results_data_file_path, 'w') as dump_file: + json.dump(data_dict, dump_file, indent=4) + +if __name__ == '__main__': + main() diff --git a/my_projects/ablation_tests/scripts/scene_test.py b/my_projects/ablation_tests/scripts/scene_test.py new file mode 100644 index 0000000000..ececb49f63 --- /dev/null +++ b/my_projects/ablation_tests/scripts/scene_test.py @@ -0,0 +1,85 @@ + +import argparse +import os +import os.path as osp + +from mmengine.config import Config, DictAction +from mmengine.runner import Runner + + +# ONLY WORKS FOR ARID20_CAT + +# load cfg +# change test_path testloader to n in [0-19] +# run test for each n +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument('config', help='train config file path') + parser.add_argument('checkpoint', help='checkpoint file') + parser.add_argument( + '--work-dir', + help=('if specified, the evaluation metric results will be dumped' + 'into the directory as json')) + args = parser.parse_args() + + + return args + + + +def main(): + args = parse_args() + + # load config + cfg = Config.fromfile(args.config) + + if args.work_dir is not None: + # update configs according to CLI args if args.work_dir is not None + cfg.work_dir = args.work_dir + elif cfg.get('work_dir', None) is None: + # use config filename as default work_dir if cfg.work_dir is None + cfg.work_dir = osp.join('./work_dirs', + osp.splitext(osp.basename(args.config))[0]) + + cfg.load_from = args.checkpoint + + work_dir = cfg.work_dir + + if not os.path.exists(work_dir): + os.makedirs(work_dir) + + # TODO implement global test + # cfg.work_dir = os.path.join(work_dir, "global") + # runner = Runner.from_cfg(cfg) + # # start testing + # runner.test() + + test_dataloader = cfg.test_dataloader + + dataset_root = test_dataloader["dataset"]["data_root"] + scene_dir_path = os.path.join(dataset_root, "img_dir", "scene") + + + + if not os.path.exists(scene_dir_path): + print(f"path {scene_dir_path} does not exists. scene test failed") + return + + for scene in os.listdir(scene_dir_path): + + + test_dataloader["dataset"]["data_prefix"] = dict( + img_path=os.path.join("img_dir", "scene", scene), + seg_map_path=os.path.join("ann_dir", "scene", scene) + ) + cfg.test_dataloader = test_dataloader + cfg.work_dir = os.path.join(work_dir, scene) + + runner = Runner.from_cfg(cfg) + + # start testing + runner.test() + + +if __name__ == '__main__': + main() diff --git a/my_projects/ablation_tests/scripts/test_all_scenes.py b/my_projects/ablation_tests/scripts/test_all_scenes.py new file mode 100644 index 0000000000..fceacfe6c5 --- /dev/null +++ b/my_projects/ablation_tests/scripts/test_all_scenes.py @@ -0,0 +1,56 @@ +import subprocess +import os +import argparse + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + '--models_path', + type=str, + default="my_projects/best_models/selection_trained/arid20_cat", + help='path of models' + ) + parser.add_argument( + '--results_path', + type=str, + help='path to store results', + default='my_projects/ablation_tests/test_results' + ) + args = parser.parse_args() + + + return args + +def main(): + args = parse_args() + for model_name in os.listdir(args.models_path): + cfg_path = os.path.join( + args.models_path, + model_name, + f"{model_name}.py" + ) + weights_path = os.path.join( + args.models_path, + model_name, + "weights.pth" + ) + work_dir_path = os.path.join( + args.results_path, + model_name + ) + subprocess.call( + [ + "python", + "my_projects/ablation_tests/scripts/scene_test.py", + cfg_path, + weights_path, + "--work-dir", + work_dir_path + ] + ) + + + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bottom/20240927_072200/20240927_072200.json b/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bottom/20240927_072200/20240927_072200.json new file mode 100644 index 0000000000..73b1d74c4e --- /dev/null +++ b/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bottom/20240927_072200/20240927_072200.json @@ -0,0 +1 @@ +{"mPr@50.0": 86.63, "mPr@60.0": 81.03, "mPr@70.0": 78.59, "mPr@80.0": 67.13, "mPr@90.0": 31.73, "mIoU": 60.93, "data_time": 0.003090524673461914, "time": 0.06091802716255188} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bottom/20240927_072200/vis_data/config.py b/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bottom/20240927_072200/vis_data/config.py new file mode 100644 index 0000000000..8e5719e373 --- /dev/null +++ b/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bottom/20240927_072200/vis_data/config.py @@ -0,0 +1,334 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/top', seg_map_path='ann_dir/scene/top'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/top' diff --git a/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bottom/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bottom/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..19b35d198e --- /dev/null +++ b/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bottom/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,335 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/bottom', + seg_map_path='ann_dir/scene/bottom'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bottom' diff --git a/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/floor/20240927_072206/20240927_072206.json b/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/floor/20240927_072206/20240927_072206.json new file mode 100644 index 0000000000..4e66632614 --- /dev/null +++ b/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/floor/20240927_072206/20240927_072206.json @@ -0,0 +1 @@ +{"mPr@50.0": 93.75, "mPr@60.0": 93.75, "mPr@70.0": 88.86, "mPr@80.0": 76.59, "mPr@90.0": 40.06, "mIoU": 78.76, "data_time": 0.005868089199066162, "time": 0.019688403606414794} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/floor/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/floor/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..17a8a03950 --- /dev/null +++ b/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/floor/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,335 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/floor', + seg_map_path='ann_dir/scene/floor'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/floor' diff --git a/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/scene_data.json b/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/scene_data.json new file mode 100644 index 0000000000..386848e388 --- /dev/null +++ b/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/scene_data.json @@ -0,0 +1,34 @@ +{ + "bottom": { + "mPr@50": 86.63, + "mPr@60": 81.03, + "mPr@70": 78.59, + "mPr@80": 67.13, + "mPr@90": 31.73, + "mIoU": 60.93 + }, + "floor": { + "mPr@50": 93.75, + "mPr@60": 93.75, + "mPr@70": 88.86, + "mPr@80": 76.59, + "mPr@90": 40.06, + "mIoU": 78.76 + }, + "table": { + "mPr@50": 85.44, + "mPr@60": 78.81, + "mPr@70": 69.08, + "mPr@80": 50.56, + "mPr@90": 17.37, + "mIoU": 69.74 + }, + "top": { + "mPr@50": 82.07, + "mPr@60": 76.1, + "mPr@70": 65.72, + "mPr@80": 47.44, + "mPr@90": 16.89, + "mIoU": 68.25 + } +} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/table/20240927_072219/20240927_072219.json b/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/table/20240927_072219/20240927_072219.json new file mode 100644 index 0000000000..7cba4ae584 --- /dev/null +++ b/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/table/20240927_072219/20240927_072219.json @@ -0,0 +1 @@ +{"mPr@50.0": 85.44, "mPr@60.0": 78.81, "mPr@70.0": 69.08, "mPr@80.0": 50.56, "mPr@90.0": 17.37, "mIoU": 69.74, "data_time": 0.0022756953199370568, "time": 0.01446598517794569} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/table/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/table/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..b4acac6863 --- /dev/null +++ b/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/table/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,335 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/table', + seg_map_path='ann_dir/scene/table'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/table' diff --git a/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/top/20240927_072232/20240927_072232.json b/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/top/20240927_072232/20240927_072232.json new file mode 100644 index 0000000000..801cf2ed70 --- /dev/null +++ b/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/top/20240927_072232/20240927_072232.json @@ -0,0 +1 @@ +{"mPr@50.0": 82.07, "mPr@60.0": 76.1, "mPr@70.0": 65.72, "mPr@80.0": 47.44, "mPr@90.0": 16.89, "mIoU": 68.25, "data_time": 0.0023919596816554213, "time": 0.015834078644261215} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/top/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/top/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..8e5719e373 --- /dev/null +++ b/my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/top/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,334 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/top', seg_map_path='ann_dir/scene/top'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/top' diff --git a/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/20240927_072250/20240927_072250.json b/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/20240927_072250/20240927_072250.json new file mode 100644 index 0000000000..87fc10fae0 --- /dev/null +++ b/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/20240927_072250/20240927_072250.json @@ -0,0 +1 @@ +{"mPr@50.0": 94.03, "mPr@60.0": 94.03, "mPr@70.0": 93.85, "mPr@80.0": 87.91, "mPr@90.0": 65.39, "mIoU": 82.07, "data_time": 0.003069913387298584, "time": 0.12094497680664062} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/20240927_072250/vis_data/config.py b/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/20240927_072250/vis_data/config.py new file mode 100644 index 0000000000..51c8416bd0 --- /dev/null +++ b/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/20240927_072250/vis_data/config.py @@ -0,0 +1,433 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/top', seg_map_path='ann_dir/scene/top'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/top' diff --git a/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..f28ad8e974 --- /dev/null +++ b/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,434 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/bottom', + seg_map_path='ann_dir/scene/bottom'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom' diff --git a/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/floor/20240927_072258/20240927_072258.json b/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/floor/20240927_072258/20240927_072258.json new file mode 100644 index 0000000000..a3dc9b085d --- /dev/null +++ b/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/floor/20240927_072258/20240927_072258.json @@ -0,0 +1 @@ +{"mPr@50.0": 99.22, "mPr@60.0": 99.22, "mPr@70.0": 98.44, "mPr@80.0": 88.76, "mPr@90.0": 69.51, "mIoU": 90.41, "data_time": 0.0060629725456237795, "time": 0.0742921233177185} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/floor/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/floor/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..554e0fdca5 --- /dev/null +++ b/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/floor/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,434 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/floor', + seg_map_path='ann_dir/scene/floor'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/floor' diff --git a/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/scene_data.json b/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/scene_data.json new file mode 100644 index 0000000000..05ef0ad8ce --- /dev/null +++ b/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/scene_data.json @@ -0,0 +1,34 @@ +{ + "bottom": { + "mPr@50": 94.03, + "mPr@60": 94.03, + "mPr@70": 93.85, + "mPr@80": 87.91, + "mPr@90": 65.39, + "mIoU": 82.07 + }, + "floor": { + "mPr@50": 99.22, + "mPr@60": 99.22, + "mPr@70": 98.44, + "mPr@80": 88.76, + "mPr@90": 69.51, + "mIoU": 90.41 + }, + "table": { + "mPr@50": 96.32, + "mPr@60": 96.32, + "mPr@70": 95.65, + "mPr@80": 88.35, + "mPr@90": 47.81, + "mIoU": 86.59 + }, + "top": { + "mPr@50": 97.2, + "mPr@60": 97.2, + "mPr@70": 96.51, + "mPr@80": 88.39, + "mPr@90": 42.14, + "mIoU": 86.53 + } +} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/table/20240927_072311/20240927_072311.json b/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/table/20240927_072311/20240927_072311.json new file mode 100644 index 0000000000..91dcd69e60 --- /dev/null +++ b/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/table/20240927_072311/20240927_072311.json @@ -0,0 +1 @@ +{"mPr@50.0": 96.32, "mPr@60.0": 96.32, "mPr@70.0": 95.65, "mPr@80.0": 88.35, "mPr@90.0": 47.81, "mIoU": 86.59, "data_time": 0.0020928282697661583, "time": 0.06861589335593857} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/table/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/table/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..4dcc67631b --- /dev/null +++ b/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/table/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,434 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/table', + seg_map_path='ann_dir/scene/table'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/table' diff --git a/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/top/20240927_072331/20240927_072331.json b/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/top/20240927_072331/20240927_072331.json new file mode 100644 index 0000000000..17b46a4433 --- /dev/null +++ b/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/top/20240927_072331/20240927_072331.json @@ -0,0 +1 @@ +{"mPr@50.0": 97.2, "mPr@60.0": 97.2, "mPr@70.0": 96.51, "mPr@80.0": 88.39, "mPr@90.0": 42.14, "mIoU": 86.53, "data_time": 0.002286918235547615, "time": 0.07146614970582904} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/top/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/top/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..51c8416bd0 --- /dev/null +++ b/my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/top/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,433 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/top', seg_map_path='ann_dir/scene/top'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/top' diff --git a/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/20240927_072354/20240927_072354.json b/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/20240927_072354/20240927_072354.json new file mode 100644 index 0000000000..625069d577 --- /dev/null +++ b/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/20240927_072354/20240927_072354.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.98, "mPr@60.0": 98.98, "mPr@70.0": 98.98, "mPr@80.0": 87.45, "mPr@90.0": 60.59, "mIoU": 85.43, "data_time": 0.0038040578365325928, "time": 0.09382016062736512} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/20240927_072354/vis_data/config.py b/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/20240927_072354/vis_data/config.py new file mode 100644 index 0000000000..f147b29adb --- /dev/null +++ b/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/20240927_072354/vis_data/config.py @@ -0,0 +1,523 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/top', seg_map_path='ann_dir/scene/top'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/top' diff --git a/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..8c8b529303 --- /dev/null +++ b/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,524 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/bottom', + seg_map_path='ann_dir/scene/bottom'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom' diff --git a/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/floor/20240927_072404/20240927_072404.json b/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/floor/20240927_072404/20240927_072404.json new file mode 100644 index 0000000000..9c5ae8fcba --- /dev/null +++ b/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/floor/20240927_072404/20240927_072404.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 100.0, "mPr@80.0": 93.53, "mPr@90.0": 60.09, "mIoU": 91.23, "data_time": 0.005703616142272949, "time": 0.05156517028808594} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/floor/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/floor/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..eaad60c640 --- /dev/null +++ b/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/floor/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,524 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/floor', + seg_map_path='ann_dir/scene/floor'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/floor' diff --git a/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/scene_data.json b/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/scene_data.json new file mode 100644 index 0000000000..c446fa5b18 --- /dev/null +++ b/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/scene_data.json @@ -0,0 +1,34 @@ +{ + "bottom": { + "mPr@50": 98.98, + "mPr@60": 98.98, + "mPr@70": 98.98, + "mPr@80": 87.45, + "mPr@90": 60.59, + "mIoU": 85.43 + }, + "floor": { + "mPr@50": 100.0, + "mPr@60": 100.0, + "mPr@70": 100.0, + "mPr@80": 93.53, + "mPr@90": 60.09, + "mIoU": 91.23 + }, + "table": { + "mPr@50": 96.09, + "mPr@60": 94.07, + "mPr@70": 92.27, + "mPr@80": 83.97, + "mPr@90": 48.5, + "mIoU": 85.3 + }, + "top": { + "mPr@50": 95.78, + "mPr@60": 93.75, + "mPr@70": 91.66, + "mPr@80": 81.6, + "mPr@90": 43.29, + "mIoU": 84.48 + } +} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/table/20240927_072419/20240927_072419.json b/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/table/20240927_072419/20240927_072419.json new file mode 100644 index 0000000000..346abe4daf --- /dev/null +++ b/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/table/20240927_072419/20240927_072419.json @@ -0,0 +1 @@ +{"mPr@50.0": 96.09, "mPr@60.0": 94.07, "mPr@70.0": 92.27, "mPr@80.0": 83.97, "mPr@90.0": 48.5, "mIoU": 85.3, "data_time": 0.0021230272886131993, "time": 0.04876429493687734} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/table/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/table/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..bfdb49f70f --- /dev/null +++ b/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/table/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,524 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/table', + seg_map_path='ann_dir/scene/table'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/table' diff --git a/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/top/20240927_072439/20240927_072439.json b/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/top/20240927_072439/20240927_072439.json new file mode 100644 index 0000000000..e62b4c2f94 --- /dev/null +++ b/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/top/20240927_072439/20240927_072439.json @@ -0,0 +1 @@ +{"mPr@50.0": 95.78, "mPr@60.0": 93.75, "mPr@70.0": 91.66, "mPr@80.0": 81.6, "mPr@90.0": 43.29, "mIoU": 84.48, "data_time": 0.0023026345956205117, "time": 0.048108633118446424} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/top/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/top/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..f147b29adb --- /dev/null +++ b/my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/top/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,523 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/top', seg_map_path='ann_dir/scene/top'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/top' diff --git a/my_projects/ablation_tests/test_results/scene_results.json b/my_projects/ablation_tests/test_results/scene_results.json new file mode 100644 index 0000000000..80fe430c91 --- /dev/null +++ b/my_projects/ablation_tests/test_results/scene_results.json @@ -0,0 +1,172 @@ +{ + "bisenetv1": { + "bottom": { + "mPr@50": 86.63, + "mPr@60": 81.03, + "mPr@70": 78.59, + "mPr@80": 67.13, + "mPr@90": 31.73, + "mIoU": 60.93 + }, + "floor": { + "mPr@50": 93.75, + "mPr@60": 93.75, + "mPr@70": 88.86, + "mPr@80": 76.59, + "mPr@90": 40.06, + "mIoU": 78.76 + }, + "table": { + "mPr@50": 85.44, + "mPr@60": 78.81, + "mPr@70": 69.08, + "mPr@80": 50.56, + "mPr@90": 17.37, + "mIoU": 69.74 + }, + "top": { + "mPr@50": 82.07, + "mPr@60": 76.1, + "mPr@70": 65.72, + "mPr@80": 47.44, + "mPr@90": 16.89, + "mIoU": 68.25 + } + }, + "mask2former": { + "bottom": { + "mPr@50": 94.03, + "mPr@60": 94.03, + "mPr@70": 93.85, + "mPr@80": 87.91, + "mPr@90": 65.39, + "mIoU": 82.07 + }, + "floor": { + "mPr@50": 99.22, + "mPr@60": 99.22, + "mPr@70": 98.44, + "mPr@80": 88.76, + "mPr@90": 69.51, + "mIoU": 90.41 + }, + "table": { + "mPr@50": 96.32, + "mPr@60": 96.32, + "mPr@70": 95.65, + "mPr@80": 88.35, + "mPr@90": 47.81, + "mIoU": 86.59 + }, + "top": { + "mPr@50": 97.2, + "mPr@60": 97.2, + "mPr@70": 96.51, + "mPr@80": 88.39, + "mPr@90": 42.14, + "mIoU": 86.53 + } + }, + "maskformer": { + "bottom": { + "mPr@50": 98.98, + "mPr@60": 98.98, + "mPr@70": 98.98, + "mPr@80": 87.45, + "mPr@90": 60.59, + "mIoU": 85.43 + }, + "floor": { + "mPr@50": 100.0, + "mPr@60": 100.0, + "mPr@70": 100.0, + "mPr@80": 93.53, + "mPr@90": 60.09, + "mIoU": 91.23 + }, + "table": { + "mPr@50": 96.09, + "mPr@60": 94.07, + "mPr@70": 92.27, + "mPr@80": 83.97, + "mPr@90": 48.5, + "mIoU": 85.3 + }, + "top": { + "mPr@50": 95.78, + "mPr@60": 93.75, + "mPr@70": 91.66, + "mPr@80": 81.6, + "mPr@90": 43.29, + "mIoU": 84.48 + } + }, + "segformer": { + "bottom": { + "mPr@50": 92.86, + "mPr@60": 91.25, + "mPr@70": 81.55, + "mPr@80": 70.58, + "mPr@90": 32.93, + "mIoU": 68.02 + }, + "floor": { + "mPr@50": 100.0, + "mPr@60": 96.65, + "mPr@70": 82.14, + "mPr@80": 71.78, + "mPr@90": 41.01, + "mIoU": 71.23 + }, + "table": { + "mPr@50": 97.19, + "mPr@60": 96.08, + "mPr@70": 93.05, + "mPr@80": 72.79, + "mPr@90": 27.14, + "mIoU": 82.68 + }, + "top": { + "mPr@50": 97.05, + "mPr@60": 93.45, + "mPr@70": 91.57, + "mPr@80": 68.87, + "mPr@90": 27.1, + "mIoU": 81.54 + } + }, + "segnext": { + "bottom": { + "mPr@50": 91.84, + "mPr@60": 89.96, + "mPr@70": 83.37, + "mPr@80": 80.03, + "mPr@90": 46.02, + "mIoU": 64.57 + }, + "floor": { + "mPr@50": 100.0, + "mPr@60": 96.96, + "mPr@70": 88.19, + "mPr@80": 80.61, + "mPr@90": 49.66, + "mIoU": 73.45 + }, + "table": { + "mPr@50": 97.1, + "mPr@60": 95.75, + "mPr@70": 91.71, + "mPr@80": 78.54, + "mPr@90": 33.26, + "mIoU": 84.03 + }, + "top": { + "mPr@50": 97.14, + "mPr@60": 93.32, + "mPr@70": 89.62, + "mPr@80": 74.35, + "mPr@90": 29.79, + "mIoU": 82.94 + } + } +} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/bottom/20240927_072502/20240927_072502.json b/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/bottom/20240927_072502/20240927_072502.json new file mode 100644 index 0000000000..7ac3c67f6a --- /dev/null +++ b/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/bottom/20240927_072502/20240927_072502.json @@ -0,0 +1 @@ +{"mPr@50.0": 92.86, "mPr@60.0": 91.25, "mPr@70.0": 81.55, "mPr@80.0": 70.58, "mPr@90.0": 32.93, "mIoU": 68.02, "data_time": 0.0035135746002197266, "time": 0.07466372847557068} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/bottom/20240927_072502/vis_data/config.py b/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/bottom/20240927_072502/vis_data/config.py new file mode 100644 index 0000000000..3cf20cc3d7 --- /dev/null +++ b/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/bottom/20240927_072502/vis_data/config.py @@ -0,0 +1,320 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/top', seg_map_path='ann_dir/scene/top'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/top' diff --git a/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/bottom/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/bottom/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..b488a8321d --- /dev/null +++ b/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/bottom/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,321 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/bottom', + seg_map_path='ann_dir/scene/bottom'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/bottom' diff --git a/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/floor/20240927_072509/20240927_072509.json b/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/floor/20240927_072509/20240927_072509.json new file mode 100644 index 0000000000..1e57527634 --- /dev/null +++ b/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/floor/20240927_072509/20240927_072509.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 96.65, "mPr@70.0": 82.14, "mPr@80.0": 71.78, "mPr@90.0": 41.01, "mIoU": 71.23, "data_time": 0.005727958679199219, "time": 0.024361443519592286} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/floor/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/floor/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..1b4ec1e17d --- /dev/null +++ b/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/floor/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,321 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/floor', + seg_map_path='ann_dir/scene/floor'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/floor' diff --git a/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/scene_data.json b/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/scene_data.json new file mode 100644 index 0000000000..a598bd0319 --- /dev/null +++ b/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/scene_data.json @@ -0,0 +1,34 @@ +{ + "bottom": { + "mPr@50": 92.86, + "mPr@60": 91.25, + "mPr@70": 81.55, + "mPr@80": 70.58, + "mPr@90": 32.93, + "mIoU": 68.02 + }, + "floor": { + "mPr@50": 100.0, + "mPr@60": 96.65, + "mPr@70": 82.14, + "mPr@80": 71.78, + "mPr@90": 41.01, + "mIoU": 71.23 + }, + "table": { + "mPr@50": 97.19, + "mPr@60": 96.08, + "mPr@70": 93.05, + "mPr@80": 72.79, + "mPr@90": 27.14, + "mIoU": 82.68 + }, + "top": { + "mPr@50": 97.05, + "mPr@60": 93.45, + "mPr@70": 91.57, + "mPr@80": 68.87, + "mPr@90": 27.1, + "mIoU": 81.54 + } +} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/table/20240927_072521/20240927_072521.json b/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/table/20240927_072521/20240927_072521.json new file mode 100644 index 0000000000..74fdf48a35 --- /dev/null +++ b/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/table/20240927_072521/20240927_072521.json @@ -0,0 +1 @@ +{"mPr@50.0": 97.19, "mPr@60.0": 96.08, "mPr@70.0": 93.05, "mPr@80.0": 72.79, "mPr@90.0": 27.14, "mIoU": 82.68, "data_time": 0.0020301923030564763, "time": 0.01920765988966998} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/table/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/table/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..ad4d97620a --- /dev/null +++ b/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/table/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,321 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/table', + seg_map_path='ann_dir/scene/table'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/table' diff --git a/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/top/20240927_072534/20240927_072534.json b/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/top/20240927_072534/20240927_072534.json new file mode 100644 index 0000000000..2089ea8327 --- /dev/null +++ b/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/top/20240927_072534/20240927_072534.json @@ -0,0 +1 @@ +{"mPr@50.0": 97.05, "mPr@60.0": 93.45, "mPr@70.0": 91.57, "mPr@80.0": 68.87, "mPr@90.0": 27.1, "mIoU": 81.54, "data_time": 0.002359790031356041, "time": 0.02025763193766276} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/top/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/top/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..3cf20cc3d7 --- /dev/null +++ b/my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/top/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,320 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/top', seg_map_path='ann_dir/scene/top'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/top' diff --git a/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/20240927_072552/20240927_072552.json b/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/20240927_072552/20240927_072552.json new file mode 100644 index 0000000000..b851744ff9 --- /dev/null +++ b/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/20240927_072552/20240927_072552.json @@ -0,0 +1 @@ +{"mPr@50.0": 91.84, "mPr@60.0": 89.96, "mPr@70.0": 83.37, "mPr@80.0": 80.03, "mPr@90.0": 46.02, "mIoU": 64.57, "data_time": 0.0033607006072998045, "time": 0.07190176844596863} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/20240927_072552/vis_data/config.py b/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/20240927_072552/vis_data/config.py new file mode 100644 index 0000000000..ddeddce68e --- /dev/null +++ b/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/20240927_072552/vis_data/config.py @@ -0,0 +1,342 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/top', seg_map_path='ann_dir/scene/top'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/top' diff --git a/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..d7f8ac9169 --- /dev/null +++ b/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,343 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/bottom', + seg_map_path='ann_dir/scene/bottom'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/bottom' diff --git a/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/floor/20240927_072559/20240927_072559.json b/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/floor/20240927_072559/20240927_072559.json new file mode 100644 index 0000000000..2b56646cff --- /dev/null +++ b/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/floor/20240927_072559/20240927_072559.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 96.96, "mPr@70.0": 88.19, "mPr@80.0": 80.61, "mPr@90.0": 49.66, "mIoU": 73.45, "data_time": 0.005599260330200195, "time": 0.027120816707611083} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/floor/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/floor/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..891a4a91c3 --- /dev/null +++ b/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/floor/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,343 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/floor', + seg_map_path='ann_dir/scene/floor'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/floor' diff --git a/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/scene_data.json b/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/scene_data.json new file mode 100644 index 0000000000..c89fa3dade --- /dev/null +++ b/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/scene_data.json @@ -0,0 +1,34 @@ +{ + "bottom": { + "mPr@50": 91.84, + "mPr@60": 89.96, + "mPr@70": 83.37, + "mPr@80": 80.03, + "mPr@90": 46.02, + "mIoU": 64.57 + }, + "floor": { + "mPr@50": 100.0, + "mPr@60": 96.96, + "mPr@70": 88.19, + "mPr@80": 80.61, + "mPr@90": 49.66, + "mIoU": 73.45 + }, + "table": { + "mPr@50": 97.1, + "mPr@60": 95.75, + "mPr@70": 91.71, + "mPr@80": 78.54, + "mPr@90": 33.26, + "mIoU": 84.03 + }, + "top": { + "mPr@50": 97.14, + "mPr@60": 93.32, + "mPr@70": 89.62, + "mPr@80": 74.35, + "mPr@90": 29.79, + "mIoU": 82.94 + } +} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/table/20240927_072611/20240927_072611.json b/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/table/20240927_072611/20240927_072611.json new file mode 100644 index 0000000000..b259391a1f --- /dev/null +++ b/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/table/20240927_072611/20240927_072611.json @@ -0,0 +1 @@ +{"mPr@50.0": 97.1, "mPr@60.0": 95.75, "mPr@70.0": 91.71, "mPr@80.0": 78.54, "mPr@90.0": 33.26, "mIoU": 84.03, "data_time": 0.002080811171972451, "time": 0.022472251363161232} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/table/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/table/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..4c044b6614 --- /dev/null +++ b/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/table/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,343 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/table', + seg_map_path='ann_dir/scene/table'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/table' diff --git a/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/top/20240927_072626/20240927_072626.json b/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/top/20240927_072626/20240927_072626.json new file mode 100644 index 0000000000..9267c5fdcc --- /dev/null +++ b/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/top/20240927_072626/20240927_072626.json @@ -0,0 +1 @@ +{"mPr@50.0": 97.14, "mPr@60.0": 93.32, "mPr@70.0": 89.62, "mPr@80.0": 74.35, "mPr@90.0": 29.79, "mIoU": 82.94, "data_time": 0.002386991423789901, "time": 0.023420608404910927} \ No newline at end of file diff --git a/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/top/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/top/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..ddeddce68e --- /dev/null +++ b/my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/top/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,342 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='img_dir/scene/top', seg_map_path='ann_dir/scene/top'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/ablation_tests/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/top' diff --git a/my_projects/best_models/configs/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512.py b/my_projects/best_models/configs/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512.py new file mode 100644 index 0000000000..870f9de7c9 --- /dev/null +++ b/my_projects/best_models/configs/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = "/media/ids/Ubuntu files/data/ARID10_CAT/" +dataset_type = "ARID10CATDataset" +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/configs/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512.py b/my_projects/best_models/configs/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512.py new file mode 100644 index 0000000000..dd78df62ee --- /dev/null +++ b/my_projects/best_models/configs/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512.py @@ -0,0 +1,428 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0 + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=10000, eta_min=0, power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/configs/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512.py b/my_projects/best_models/configs/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512.py new file mode 100644 index 0000000000..4b83bef0ea --- /dev/null +++ b/my_projects/best_models/configs/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0 + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/configs/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512.py b/my_projects/best_models/configs/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512.py new file mode 100644 index 0000000000..0e1b1519e4 --- /dev/null +++ b/my_projects/best_models/configs/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/configs/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512.py b/my_projects/best_models/configs/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512.py new file mode 100644 index 0000000000..f3edc434db --- /dev/null +++ b/my_projects/best_models/configs/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/configs/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/best_models/configs/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..898ce10592 --- /dev/null +++ b/my_projects/best_models/configs/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,336 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = "/media/ids/Ubuntu files/data/ARID20_CAT/" +dataset_type = "ARID20CATDataset" +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/configs/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/best_models/configs/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..d4297e3bbd --- /dev/null +++ b/my_projects/best_models/configs/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,431 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0 + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=10000, eta_min=0, power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/configs/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/best_models/configs/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..2b6033c364 --- /dev/null +++ b/my_projects/best_models/configs/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,525 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0 + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/configs/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/best_models/configs/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..1497b40414 --- /dev/null +++ b/my_projects/best_models/configs/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,322 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/configs/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/best_models/configs/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..a8f9dbb8fa --- /dev/null +++ b/my_projects/best_models/configs/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,344 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/configs/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512.py b/my_projects/best_models/configs/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..d70af2edfb --- /dev/null +++ b/my_projects/best_models/configs/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512.py @@ -0,0 +1,336 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512' diff --git a/my_projects/best_models/configs/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512.py b/my_projects/best_models/configs/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..0d492a7489 --- /dev/null +++ b/my_projects/best_models/configs/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512.py @@ -0,0 +1,446 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=10000, eta_min=0, power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/best_models/configs/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512.py b/my_projects/best_models/configs/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..361d3ce964 --- /dev/null +++ b/my_projects/best_models/configs/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512.py @@ -0,0 +1,540 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/best_models/configs/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512.py b/my_projects/best_models/configs/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..ac7740e8e3 --- /dev/null +++ b/my_projects/best_models/configs/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512.py @@ -0,0 +1,322 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_hots-v1-512x512' diff --git a/my_projects/best_models/configs/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512.py b/my_projects/best_models/configs/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..4bb3607a96 --- /dev/null +++ b/my_projects/best_models/configs/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512.py @@ -0,0 +1,344 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=47, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/best_models/configs/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512.py b/my_projects/best_models/configs/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512.py new file mode 100644 index 0000000000..e3d271ca8c --- /dev/null +++ b/my_projects/best_models/configs/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/" +data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/" +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/", + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/", + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/", + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/configs/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py b/my_projects/best_models/configs/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py new file mode 100644 index 0000000000..7c8b96dc1d --- /dev/null +++ b/my_projects/best_models/configs/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py @@ -0,0 +1,422 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/" +data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/" +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0 + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=10000, eta_min=0, power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/", + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/", + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/", + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/configs/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py b/my_projects/best_models/configs/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py new file mode 100644 index 0000000000..99fffd2241 --- /dev/null +++ b/my_projects/best_models/configs/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py @@ -0,0 +1,516 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/" +data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/" +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0 + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/", + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/", + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/", + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/configs/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512.py b/my_projects/best_models/configs/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512.py new file mode 100644 index 0000000000..ab2bcf3bd7 --- /dev/null +++ b/my_projects/best_models/configs/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/" +data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/" +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/", + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/", + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/", + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/configs/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py b/my_projects/best_models/configs/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py new file mode 100644 index 0000000000..a882866548 --- /dev/null +++ b/my_projects/best_models/configs/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/" +data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/" +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=26, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/", + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/", + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root = "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/", + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/configs/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512.py b/my_projects/best_models/configs/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..61b8d1b855 --- /dev/null +++ b/my_projects/best_models/configs/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512.py @@ -0,0 +1,339 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512' diff --git a/my_projects/best_models/configs/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py b/my_projects/best_models/configs/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..86656f545a --- /dev/null +++ b/my_projects/best_models/configs/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py @@ -0,0 +1,474 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=10000, eta_min=0, power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/best_models/configs/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py b/my_projects/best_models/configs/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..eeb13dda5e --- /dev/null +++ b/my_projects/best_models/configs/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py @@ -0,0 +1,568 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/best_models/configs/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512.py b/my_projects/best_models/configs/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..6e38fd3263 --- /dev/null +++ b/my_projects/best_models/configs/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512.py @@ -0,0 +1,325 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512' diff --git a/my_projects/best_models/configs/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py b/my_projects/best_models/configs/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..0b44c735cf --- /dev/null +++ b/my_projects/best_models/configs/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py @@ -0,0 +1,347 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=72, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim-512x512' diff --git a/my_projects/best_models/configs/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512.py b/my_projects/best_models/configs/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..85bf65a9e5 --- /dev/null +++ b/my_projects/best_models/configs/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,336 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/configs/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py b/my_projects/best_models/configs/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..514ed4a855 --- /dev/null +++ b/my_projects/best_models/configs/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,429 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0 + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=10000, eta_min=0, power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/configs/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py b/my_projects/best_models/configs/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..a2bb8bcda0 --- /dev/null +++ b/my_projects/best_models/configs/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,523 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0 + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/configs/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py b/my_projects/best_models/configs/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..12ffd33c35 --- /dev/null +++ b/my_projects/best_models/configs/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,322 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/configs/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py b/my_projects/best_models/configs/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..26845466bd --- /dev/null +++ b/my_projects/best_models/configs/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,344 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=30, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/configs/irl_vision_cat_2k/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512.py b/my_projects/best_models/configs/irl_vision_cat_2k/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..d0e0bcd2e7 --- /dev/null +++ b/my_projects/best_models/configs/irl_vision_cat_2k/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,336 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=2000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/configs/irl_vision_cat_2k/mask2former_r50_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512.py b/my_projects/best_models/configs/irl_vision_cat_2k/mask2former_r50_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..801e26a31c --- /dev/null +++ b/my_projects/best_models/configs/irl_vision_cat_2k/mask2former_r50_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,429 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0 + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=10000, eta_min=0, power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=2000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/configs/irl_vision_cat_2k/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512.py b/my_projects/best_models/configs/irl_vision_cat_2k/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..f252be1ecf --- /dev/null +++ b/my_projects/best_models/configs/irl_vision_cat_2k/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,523 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0 + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=2000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/configs/irl_vision_cat_2k/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512.py b/my_projects/best_models/configs/irl_vision_cat_2k/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..76aa9cbe2c --- /dev/null +++ b/my_projects/best_models/configs/irl_vision_cat_2k/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,322 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=2000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/configs/irl_vision_cat_2k/segnext_mscan-t_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512.py b/my_projects/best_models/configs/irl_vision_cat_2k/segnext_mscan-t_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..5a54f6c9d7 --- /dev/null +++ b/my_projects/best_models/configs/irl_vision_cat_2k/segnext_mscan-t_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,344 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=30, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=2000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/configs/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512.py b/my_projects/best_models/configs/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..798867aadb --- /dev/null +++ b/my_projects/best_models/configs/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,337 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/configs/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py b/my_projects/best_models/configs/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..e4f9076d76 --- /dev/null +++ b/my_projects/best_models/configs/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,430 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0 + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=10000, eta_min=0, power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/configs/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py b/my_projects/best_models/configs/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..b0cddb74ab --- /dev/null +++ b/my_projects/best_models/configs/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,524 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0 + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/configs/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py b/my_projects/best_models/configs/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..4be1a1ecf7 --- /dev/null +++ b/my_projects/best_models/configs/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,323 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/configs/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py b/my_projects/best_models/configs/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..ec0943130d --- /dev/null +++ b/my_projects/best_models/configs/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,345 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=30, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/configs/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512.py b/my_projects/best_models/configs/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512.py new file mode 100644 index 0000000000..6767f725df --- /dev/null +++ b/my_projects/best_models/configs/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512.py @@ -0,0 +1,336 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOT-C/SemanticSegmentation/' +dataset_type = 'SODHOTCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/SODHOT-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/SODHOT-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/SODHOT-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhot-c-512x512' diff --git a/my_projects/best_models/configs/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512.py b/my_projects/best_models/configs/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512.py new file mode 100644 index 0000000000..282b5bbe12 --- /dev/null +++ b/my_projects/best_models/configs/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512.py @@ -0,0 +1,430 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0 + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=31, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 31 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=10000, eta_min=0, power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512' diff --git a/my_projects/best_models/configs/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512.py b/my_projects/best_models/configs/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512.py new file mode 100644 index 0000000000..2187a6de58 --- /dev/null +++ b/my_projects/best_models/configs/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512.py @@ -0,0 +1,524 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0 + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=31, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 31 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512' diff --git a/my_projects/best_models/configs/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512.py b/my_projects/best_models/configs/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512.py new file mode 100644 index 0000000000..1026c2142c --- /dev/null +++ b/my_projects/best_models/configs/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512.py @@ -0,0 +1,322 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_sodhots-c-512x512' diff --git a/my_projects/best_models/configs/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512.py b/my_projects/best_models/configs/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512.py new file mode 100644 index 0000000000..2e8654ee85 --- /dev/null +++ b/my_projects/best_models/configs/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512.py @@ -0,0 +1,344 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=31, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512' diff --git a/my_projects/best_models/original_models/bisenetv1_r18-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024/bisenetv1_r18-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py b/my_projects/best_models/original_models/bisenetv1_r18-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024/bisenetv1_r18-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py new file mode 100644 index 0000000000..0f3a525caa --- /dev/null +++ b/my_projects/best_models/original_models/bisenetv1_r18-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024/bisenetv1_r18-d32-in1k-pre_4xb4-160k_cityscapes-1024x1024.py @@ -0,0 +1,330 @@ +crop_size = ( + 1024, + 1024, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 1024, + 1024, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cityscapes/' +dataset_type = 'CityscapesDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=19, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=19, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 1024, + 1024, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=19, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.025, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.025, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1000, start_factor=0.1, type='LinearLR'), + dict( + begin=1000, + by_epoch=False, + end=160000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='leftImg8bit/val', seg_map_path='gtFine/val'), + data_root='data/cityscapes/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 1024, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='CityscapesDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 1024, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='leftImg8bit/train', seg_map_path='gtFine/train'), + data_root='data/cityscapes/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 1024, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, + crop_size=( + 1024, + 1024, + ), + type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='CityscapesDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 1024, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 1024, + 1024, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='leftImg8bit/val', seg_map_path='gtFine/val'), + data_root='data/cityscapes/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 1024, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='CityscapesDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) diff --git a/my_projects/best_models/original_models/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py b/my_projects/best_models/original_models/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py new file mode 100644 index 0000000000..13ac04cdfb --- /dev/null +++ b/my_projects/best_models/original_models/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py @@ -0,0 +1,328 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/coco_stuff164k' +dataset_type = 'COCOStuffDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=171, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=171, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=171, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1000, start_factor=0.1, type='LinearLR'), + dict( + begin=1000, + by_epoch=False, + end=160000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/val2017', seg_map_path='annotations/val2017'), + data_root='data/coco_stuff164k', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='COCOStuffDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='images/train2017', seg_map_path='annotations/train2017'), + data_root='data/coco_stuff164k', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='COCOStuffDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/val2017', seg_map_path='annotations/val2017'), + data_root='data/coco_stuff164k', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='COCOStuffDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) diff --git a/my_projects/best_models/original_models/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512.py b/my_projects/best_models/original_models/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..ef2e43aa44 --- /dev/null +++ b/my_projects/best_models/original_models/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,571 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ADEChallengeData2016/' +dataset_type = 'ADE20KDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.1, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=150, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 150 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=160000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=5000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + max_size=2048, + resize_type='ResizeShortestEdge', + scales=[ + 256, + 307, + 358, + 409, + 460, + 512, + 563, + 614, + 665, + 716, + 768, + 819, + 870, + 921, + 972, + 1024, + ], + type='RandomChoiceResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + max_size=2048, + resize_type='ResizeShortestEdge', + scales=[ + 256, + 307, + 358, + 409, + 460, + 512, + 563, + 614, + 665, + 716, + 768, + 819, + 870, + 921, + 972, + 1024, + ], + type='RandomChoiceResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) diff --git a/my_projects/best_models/original_models/mask2former_r50_8xb2-90k_cityscapes-512x1024/mask2former_r50_8xb2-90k_cityscapes-512x1024.py b/my_projects/best_models/original_models/mask2former_r50_8xb2-90k_cityscapes-512x1024/mask2former_r50_8xb2-90k_cityscapes-512x1024.py new file mode 100644 index 0000000000..061da164bf --- /dev/null +++ b/my_projects/best_models/original_models/mask2former_r50_8xb2-90k_cityscapes-512x1024/mask2former_r50_8xb2-90k_cityscapes-512x1024.py @@ -0,0 +1,436 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 1024, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 1024, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = 'data/cityscapes/' +dataset_type = 'CityscapesDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 1024, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.1, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=19, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 19 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=90000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='leftImg8bit/val', seg_map_path='gtFine/val'), + data_root='data/cityscapes/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 1024, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='CityscapesDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 1024, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=90000, type='IterBasedTrainLoop', val_interval=5000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='leftImg8bit/train', seg_map_path='gtFine/train'), + data_root='data/cityscapes/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + max_size=4096, + resize_type='ResizeShortestEdge', + scales=[ + 512, + 614, + 716, + 819, + 921, + 1024, + 1126, + 1228, + 1331, + 1433, + 1536, + 1638, + 1740, + 1843, + 1945, + 2048, + ], + type='RandomChoiceResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 1024, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='CityscapesDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + max_size=4096, + resize_type='ResizeShortestEdge', + scales=[ + 512, + 614, + 716, + 819, + 921, + 1024, + 1126, + 1228, + 1331, + 1433, + 1536, + 1638, + 1740, + 1843, + 1945, + 2048, + ], + type='RandomChoiceResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 1024, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='leftImg8bit/val', seg_map_path='gtFine/val'), + data_root='data/cityscapes/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 1024, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='CityscapesDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) diff --git a/my_projects/best_models/original_models/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py b/my_projects/best_models/original_models/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..3b3c3941be --- /dev/null +++ b/my_projects/best_models/original_models/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,634 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ADEChallengeData2016/' +dataset_type = 'ADE20KDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=224, + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.1, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=150, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 150 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) diff --git a/my_projects/best_models/original_models/segformer_mit-b0_8xb1-160k_cityscapes-1024x1024/segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py b/my_projects/best_models/original_models/segformer_mit-b0_8xb1-160k_cityscapes-1024x1024/segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py new file mode 100644 index 0000000000..d6c8f52f39 --- /dev/null +++ b/my_projects/best_models/original_models/segformer_mit-b0_8xb1-160k_cityscapes-1024x1024/segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py @@ -0,0 +1,322 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 1024, + 1024, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 1024, + 1024, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/cityscapes/' +dataset_type = 'CityscapesDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 1024, + 1024, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=19, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(crop_size=( + 1024, + 1024, + ), mode='slide', stride=( + 768, + 768, + )), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='leftImg8bit/val', seg_map_path='gtFine/val'), + data_root='data/cityscapes/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 1024, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='CityscapesDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 1024, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='leftImg8bit/train', seg_map_path='gtFine/train'), + data_root='data/cityscapes/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 1024, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, + crop_size=( + 1024, + 1024, + ), + type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='CityscapesDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 1024, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 1024, + 1024, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='leftImg8bit/val', seg_map_path='gtFine/val'), + data_root='data/cityscapes/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 1024, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='CityscapesDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) diff --git a/my_projects/best_models/original_models/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_mit-b0_8xb2-160k_ade20k-512x512.py b/my_projects/best_models/original_models/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_mit-b0_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..0b220e8268 --- /dev/null +++ b/my_projects/best_models/original_models/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_mit-b0_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,316 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ADEChallengeData2016/' +dataset_type = 'ADE20KDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) diff --git a/my_projects/best_models/original_models/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py b/my_projects/best_models/original_models/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py new file mode 100644 index 0000000000..466d3ab7c7 --- /dev/null +++ b/my_projects/best_models/original_models/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py @@ -0,0 +1,338 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ADEChallengeData2016/' +dataset_type = 'ADE20KDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=150, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=16, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) diff --git a/my_projects/best_models/selection_trained/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/20241107_100923/vis_data/20241107_100923.json b/my_projects/best_models/selection_trained/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/20241107_100923/vis_data/20241107_100923.json new file mode 100644 index 0000000000..b87f7144ac --- /dev/null +++ b/my_projects/best_models/selection_trained/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/20241107_100923/vis_data/20241107_100923.json @@ -0,0 +1,210 @@ +{"lr": 0.0006470980653769185, "data_time": 0.002447199821472168, "loss": 1.8389300942420959, "decode.loss_ce": 0.5951052665710449, "decode.acc_seg": 72.60600280761719, "aux_0.loss_ce": 0.6109003871679306, "aux_0.acc_seg": 72.60600280761719, "aux_1.loss_ce": 0.6329244717955589, "aux_1.acc_seg": 72.60600280761719, "time": 0.10100593566894531, "iter": 50, "memory": 2548, "step": 50} +{"lr": 0.0007971981320880594, "data_time": 0.0024142026901245116, "loss": 1.7893561661243438, "decode.loss_ce": 0.6141090601682663, "decode.acc_seg": 82.52318572998047, 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0.10473625659942627, "iter": 10000, "memory": 834, "step": 10000} +{"aAcc": 97.42, "mIoU": 55.3, "mAcc": 68.67, "data_time": 0.00150830254835241, "time": 0.013564120320712818, "step": 10000} diff --git a/my_projects/best_models/selection_trained/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/20241107_100923/vis_data/config.py b/my_projects/best_models/selection_trained/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/20241107_100923/vis_data/config.py new file mode 100644 index 0000000000..6ecb6abf7a --- /dev/null +++ b/my_projects/best_models/selection_trained/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/20241107_100923/vis_data/config.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/selection_trained/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/20241107_100923/vis_data/scalars.json b/my_projects/best_models/selection_trained/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/20241107_100923/vis_data/scalars.json new file mode 100644 index 0000000000..b87f7144ac --- /dev/null +++ b/my_projects/best_models/selection_trained/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/20241107_100923/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"lr": 0.0006470980653769185, "data_time": 0.002447199821472168, "loss": 1.8389300942420959, "decode.loss_ce": 0.5951052665710449, "decode.acc_seg": 72.60600280761719, "aux_0.loss_ce": 0.6109003871679306, 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0000000000..6ecb6abf7a --- /dev/null +++ b/my_projects/best_models/selection_trained/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/selection_trained/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_102748/vis_data/20241107_102748.json b/my_projects/best_models/selection_trained/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_102748/vis_data/20241107_102748.json new file mode 100644 index 0000000000..406185768d --- /dev/null +++ b/my_projects/best_models/selection_trained/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_102748/vis_data/20241107_102748.json @@ -0,0 +1,210 @@ +{"base_lr": 9.955884763429715e-05, "lr": 9.955884763429715e-06, "data_time": 0.005306100845336914, "grad_norm": 275.26212310791016, "loss": 25.957440185546876, "decode.loss_cls": 0.6028118401765823, "decode.loss_mask": 0.4600101500749588, "decode.loss_dice": 0.7139581501483917, "decode.d0.loss_cls": 7.076222896575928, "decode.d0.loss_mask": 0.7174189567565918, "decode.d0.loss_dice": 1.1237005084753036, "decode.d1.loss_cls": 0.6498780250549316, "decode.d1.loss_mask": 0.6193289458751678, "decode.d1.loss_dice": 0.8730206161737442, "decode.d2.loss_cls": 0.634550815820694, "decode.d2.loss_mask": 0.541669151186943, "decode.d2.loss_dice": 0.7963675856590271, "decode.d3.loss_cls": 0.6315273612737655, "decode.d3.loss_mask": 0.5092857867479325, "decode.d3.loss_dice": 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dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/selection_trained/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_102748/vis_data/scalars.json b/my_projects/best_models/selection_trained/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_102748/vis_data/scalars.json new file mode 100644 index 0000000000..406185768d --- /dev/null +++ b/my_projects/best_models/selection_trained/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_102748/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 9.955884763429715e-05, "lr": 9.955884763429715e-06, "data_time": 0.005306100845336914, "grad_norm": 275.26212310791016, "loss": 25.957440185546876, "decode.loss_cls": 0.6028118401765823, "decode.loss_mask": 0.4600101500749588, "decode.loss_dice": 0.7139581501483917, "decode.d0.loss_cls": 7.076222896575928, "decode.d0.loss_mask": 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a/my_projects/best_models/selection_trained/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512.py b/my_projects/best_models/selection_trained/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512.py new file mode 100644 index 0000000000..e6a35be62c --- /dev/null +++ b/my_projects/best_models/selection_trained/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/20241107_113810.json b/my_projects/best_models/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/20241107_113810.json new file mode 100644 index 0000000000..0ededb5788 --- /dev/null +++ b/my_projects/best_models/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/20241107_113810.json @@ -0,0 +1,210 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 0.005614161491394043, "grad_norm": 518.7013122558594, "loss": 22.32619705200195, "decode.loss_cls": 3.158992862701416, "decode.loss_mask": 0.20419039502739905, "decode.loss_dice": 0.4701965257525444, "decode.d0.loss_cls": 2.833039093017578, "decode.d0.loss_mask": 0.19366986453533172, "decode.d0.loss_dice": 0.46704602241516113, "decode.d1.loss_cls": 2.9835837125778197, "decode.d1.loss_mask": 0.1987227037549019, "decode.d1.loss_dice": 0.48906650245189665, "decode.d2.loss_cls": 3.2082458257675173, 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a/my_projects/best_models/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/config.py b/my_projects/best_models/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/config.py new file mode 100644 index 0000000000..2d10c53a59 --- /dev/null +++ b/my_projects/best_models/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/config.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/scalars.json b/my_projects/best_models/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/scalars.json new file mode 100644 index 0000000000..0ededb5788 --- /dev/null +++ b/my_projects/best_models/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 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b/my_projects/best_models/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/selection_trained/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_122327/vis_data/20241107_122327.json b/my_projects/best_models/selection_trained/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_122327/vis_data/20241107_122327.json new file mode 100644 index 0000000000..c47556136f --- /dev/null +++ b/my_projects/best_models/selection_trained/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_122327/vis_data/20241107_122327.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.0035311222076416016, "loss": 3.044075846672058, "decode.loss_ce": 3.044075846672058, "decode.acc_seg": 5.998446941375732, "time": 0.10401318073272706, "iter": 50, "memory": 2068, "step": 50} +{"base_lr": 3.962697798532355e-06, "lr": 3.962697798532355e-06, "data_time": 0.003507566452026367, "loss": 2.8921288728713987, "decode.loss_ce": 2.8921288728713987, "decode.acc_seg": 66.02977752685547, 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diff --git a/my_projects/best_models/selection_trained/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_122327/vis_data/config.py b/my_projects/best_models/selection_trained/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_122327/vis_data/config.py new file mode 100644 index 0000000000..0e1b1519e4 --- /dev/null +++ b/my_projects/best_models/selection_trained/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_122327/vis_data/config.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/selection_trained/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_122327/vis_data/scalars.json b/my_projects/best_models/selection_trained/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_122327/vis_data/scalars.json new file mode 100644 index 0000000000..c47556136f --- /dev/null +++ b/my_projects/best_models/selection_trained/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_122327/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.0035311222076416016, "loss": 3.044075846672058, "decode.loss_ce": 3.044075846672058, "decode.acc_seg": 5.998446941375732, "time": 0.10401318073272706, "iter": 50, "memory": 2068, 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+ pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/20241107_124022.json b/my_projects/best_models/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/20241107_124022.json new file mode 100644 index 0000000000..8fe9a66cbd --- /dev/null +++ b/my_projects/best_models/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/20241107_124022.json @@ -0,0 +1,210 @@ 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"loss": 0.03264338420704007, "decode.loss_ce": 0.03264338420704007, "decode.acc_seg": 99.69121551513672, "time": 0.14220120906829833, "iter": 10000, "memory": 1344, "step": 10000} +{"aAcc": 98.88, "mIoU": 76.15, "mAcc": 83.11, "data_time": 0.0014969846781562356, "time": 0.02160899428760304, "step": 10000} diff --git a/my_projects/best_models/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/config.py b/my_projects/best_models/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/config.py new file mode 100644 index 0000000000..f3edc434db --- /dev/null +++ b/my_projects/best_models/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/config.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/scalars.json b/my_projects/best_models/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/scalars.json new file mode 100644 index 0000000000..8fe9a66cbd --- /dev/null +++ b/my_projects/best_models/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.005095911026000976, "loss": 2.9879141569137575, "decode.loss_ce": 2.9879141569137575, "decode.acc_seg": 0.021314797922968864, "time": 0.15326998233795167, "iter": 50, "memory": 3230, "step": 50} +{"base_lr": 3.962697798532355e-06, "lr": 3.962697798532355e-06, "data_time": 0.004787039756774902, "loss": 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a/my_projects/best_models/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512.py b/my_projects/best_models/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512.py new file mode 100644 index 0000000000..f3edc434db --- /dev/null +++ b/my_projects/best_models/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20240917_114220/vis_data/20240917_114220.json b/my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20240917_114220/vis_data/20240917_114220.json new file mode 100644 index 0000000000..8708068d5d --- /dev/null +++ b/my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20240917_114220/vis_data/20240917_114220.json @@ -0,0 +1,210 @@ +{"lr": 0.0006470980653769185, "data_time": 0.0023443222045898436, "loss": 3.194075566530228, "decode.loss_ce": 1.0545195966959, "decode.acc_seg": 58.623165130615234, "aux_0.loss_ce": 1.0397097498178482, "aux_0.acc_seg": 58.688602447509766, "aux_1.loss_ce": 1.0998461604118348, "aux_1.acc_seg": 58.62216567993164, "time": 0.09662184715270997, "iter": 50, "memory": 2548, "step": 50} +{"lr": 0.0007971981320880594, "data_time": 0.002430558204650879, "loss": 3.3274322390556335, "decode.loss_ce": 1.1332894623279572, "decode.acc_seg": 76.62688446044922, 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a/my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20240917_114220/vis_data/config.py b/my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20240917_114220/vis_data/config.py new file mode 100644 index 0000000000..ac7ab78d2d --- /dev/null +++ b/my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20240917_114220/vis_data/config.py @@ -0,0 +1,336 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20240917_114220/vis_data/scalars.json b/my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20240917_114220/vis_data/scalars.json new file mode 100644 index 0000000000..8708068d5d --- /dev/null +++ b/my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20240917_114220/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"lr": 0.0006470980653769185, "data_time": 0.0023443222045898436, "loss": 3.194075566530228, "decode.loss_ce": 1.0545195966959, "decode.acc_seg": 58.623165130615234, "aux_0.loss_ce": 1.0397097498178482, "aux_0.acc_seg": 58.688602447509766, "aux_1.loss_ce": 1.0998461604118348, "aux_1.acc_seg": 58.62216567993164, "time": 0.09662184715270997, "iter": 50, "memory": 2548, "step": 50} +{"lr": 0.0007971981320880594, 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"data_time": 0.0014096999168395996, "time": 0.013367478847503661, "step": 10000} diff --git a/my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..bff6f6bba1 --- /dev/null +++ b/my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_161558/vis_data/20240917_161558.json b/my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_161558/vis_data/20240917_161558.json new file mode 100644 index 0000000000..b19363571b --- /dev/null +++ b/my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_161558/vis_data/20240917_161558.json @@ -0,0 +1,210 @@ +{"base_lr": 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b/my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_161558/vis_data/config.py @@ -0,0 +1,435 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_161558/vis_data/scalars.json b/my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_161558/vis_data/scalars.json new file mode 100644 index 0000000000..b19363571b --- /dev/null +++ b/my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_161558/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 9.955884763429715e-05, "lr": 9.955884763429715e-06, "data_time": 0.005344867706298828, "grad_norm": 194.01115036010742, "loss": 26.501869201660156, 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0.18552006930112838, "decode.d8.loss_cls": 0.011097147088003112, "decode.d8.loss_mask": 0.11716443970799446, "decode.d8.loss_dice": 0.17722982168197632, "time": 0.4180600166320801, "iter": 10000, "memory": 2995, "step": 10000} +{"aAcc": 98.93, "mIoU": 85.96, "mAcc": 91.65, "data_time": 0.0014711809158325195, "time": 0.07034803152084351, "step": 10000} diff --git a/my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..f18f3142a6 --- /dev/null +++ b/my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/20240917_172715.json b/my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/20240917_172715.json new file mode 100644 index 0000000000..198798d077 --- /dev/null +++ b/my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/20240917_172715.json @@ -0,0 +1,210 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 0.0051671028137207035, "grad_norm": 470.30755310058595, "loss": 23.879179000854492, "decode.loss_cls": 3.3874343633651733, "decode.loss_mask": 0.17753420174121856, "decode.loss_dice": 0.3346044570207596, "decode.d0.loss_cls": 3.4928240299224855, 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0.027155735064297915, "decode.d0.loss_dice": 0.05359881930053234, "decode.d1.loss_cls": 0.015642854746693045, "decode.d1.loss_mask": 0.02589314989745617, "decode.d1.loss_dice": 0.05301286745816469, "decode.d2.loss_cls": 0.014333615126815857, "decode.d2.loss_mask": 0.025957035832107067, "decode.d2.loss_dice": 0.05428182240575552, "decode.d3.loss_cls": 0.014675870265637058, "decode.d3.loss_mask": 0.025721714459359647, "decode.d3.loss_dice": 0.05478774532675743, "decode.d4.loss_cls": 0.014244628862434184, "decode.d4.loss_mask": 0.02554449802264571, "decode.d4.loss_dice": 0.05509538426995277, "time": 0.26356964111328124, "iter": 10000, "memory": 3067, "step": 10000} +{"aAcc": 98.95, "mIoU": 87.52, "mAcc": 93.06, "data_time": 0.0014193153381347656, "time": 0.04673076629638672, "step": 10000} diff --git a/my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/config.py b/my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/config.py new file mode 100644 index 0000000000..df7a8dc4f0 --- /dev/null +++ b/my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/config.py @@ -0,0 +1,525 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/scalars.json b/my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/scalars.json new file mode 100644 index 0000000000..198798d077 --- /dev/null +++ b/my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 0.0051671028137207035, "grad_norm": 470.30755310058595, "loss": 23.879179000854492, "decode.loss_cls": 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"data_time": 0.0014193153381347656, "time": 0.04673076629638672, "step": 10000} diff --git a/my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..e07ae267ce --- /dev/null +++ b/my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/20240917_120056/vis_data/20240917_120056.json b/my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/20240917_120056/vis_data/20240917_120056.json new file mode 100644 index 0000000000..bb247aadd2 --- /dev/null +++ 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b/my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/20240917_120056/vis_data/config.py @@ -0,0 +1,322 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_arid20_cat-512x512' diff --git 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a/my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..0d9ad30ef8 --- /dev/null +++ b/my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/20240917_121739.json b/my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/20240917_121739.json new file mode 100644 index 0000000000..cc70a96776 --- /dev/null +++ b/my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/20240917_121739.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.004879283905029297, "loss": 3.111197996139526, "decode.loss_ce": 3.111197996139526, "decode.acc_seg": 58.2942008972168, 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0.0014802050590515137, "time": 0.023037610054016115, "step": 10000} diff --git a/my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/config.py b/my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/config.py new file mode 100644 index 0000000000..a8f9dbb8fa --- /dev/null +++ b/my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/config.py @@ -0,0 +1,344 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/scalars.json b/my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/scalars.json new file mode 100644 index 0000000000..cc70a96776 --- /dev/null +++ b/my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.004879283905029297, "loss": 3.111197996139526, "decode.loss_ce": 3.111197996139526, "decode.acc_seg": 58.2942008972168, "time": 0.15078768730163575, "iter": 50, "memory": 3230, "step": 50} +{"base_lr": 3.962697798532355e-06, "lr": 3.962697798532355e-06, "data_time": 0.004775619506835938, "loss": 2.777503323554993, "decode.loss_ce": 2.777503323554993, "decode.acc_seg": 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a/my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..344a86a610 --- /dev/null +++ b/my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/20241107_100923/vis_data/20241107_100923.json b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/20241107_100923/vis_data/20241107_100923.json new file mode 100644 index 0000000000..b87f7144ac --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/20241107_100923/vis_data/20241107_100923.json @@ -0,0 +1,210 @@ +{"lr": 0.0006470980653769185, "data_time": 0.002447199821472168, "loss": 1.8389300942420959, "decode.loss_ce": 0.5951052665710449, "decode.acc_seg": 72.60600280761719, "aux_0.loss_ce": 0.6109003871679306, "aux_0.acc_seg": 72.60600280761719, "aux_1.loss_ce": 0.6329244717955589, "aux_1.acc_seg": 72.60600280761719, "time": 0.10100593566894531, "iter": 50, "memory": 2548, "step": 50} +{"lr": 0.0007971981320880594, "data_time": 0.0024142026901245116, "loss": 1.7893561661243438, "decode.loss_ce": 0.6141090601682663, "decode.acc_seg": 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std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/20241107_100923/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/20241107_100923/vis_data/scalars.json new file mode 100644 index 0000000000..b87f7144ac --- /dev/null +++ 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a/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512.py new file mode 100644 index 0000000000..6ecb6abf7a --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_102748/vis_data/20241107_102748.json b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_102748/vis_data/20241107_102748.json new file mode 100644 index 0000000000..406185768d --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_102748/vis_data/20241107_102748.json @@ -0,0 +1,210 @@ +{"base_lr": 9.955884763429715e-05, "lr": 9.955884763429715e-06, "data_time": 0.005306100845336914, "grad_norm": 275.26212310791016, "loss": 25.957440185546876, "decode.loss_cls": 0.6028118401765823, "decode.loss_mask": 0.4600101500749588, "decode.loss_dice": 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a/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_102748/vis_data/config.py b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_102748/vis_data/config.py new file mode 100644 index 0000000000..e6a35be62c --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_102748/vis_data/config.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_102748/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_102748/vis_data/scalars.json new file mode 100644 index 0000000000..406185768d --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_102748/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 9.955884763429715e-05, "lr": 9.955884763429715e-06, "data_time": 0.005306100845336914, "grad_norm": 275.26212310791016, "loss": 25.957440185546876, "decode.loss_cls": 0.6028118401765823, "decode.loss_mask": 0.4600101500749588, "decode.loss_dice": 0.7139581501483917, "decode.d0.loss_cls": 7.076222896575928, "decode.d0.loss_mask": 0.7174189567565918, "decode.d0.loss_dice": 1.1237005084753036, "decode.d1.loss_cls": 0.6498780250549316, "decode.d1.loss_mask": 0.6193289458751678, "decode.d1.loss_dice": 0.8730206161737442, "decode.d2.loss_cls": 0.634550815820694, "decode.d2.loss_mask": 0.541669151186943, "decode.d2.loss_dice": 0.7963675856590271, "decode.d3.loss_cls": 0.6315273612737655, "decode.d3.loss_mask": 0.5092857867479325, "decode.d3.loss_dice": 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0.0005188154485949781, "decode.d7.loss_mask": 0.11622770875692368, "decode.d7.loss_dice": 0.18020443022251129, "decode.d8.loss_cls": 0.0005204445114941336, "decode.d8.loss_mask": 0.11527794301509857, "decode.d8.loss_dice": 0.18376239091157914, "time": 0.41012618541717527, "iter": 10000, "memory": 2923, "step": 10000} +{"aAcc": 98.93, "mIoU": 83.01, "mAcc": 89.23, "data_time": 0.0015466633964987362, "time": 0.07242078640881706, "step": 10000} diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512.py new file mode 100644 index 0000000000..e6a35be62c --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/20241107_113810.json b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/20241107_113810.json new file mode 100644 index 0000000000..0ededb5788 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/20241107_113810.json @@ -0,0 +1,210 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 0.005614161491394043, 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"decode.d2.loss_dice": 0.04586559738963843, "decode.d3.loss_cls": 0.014563255199755076, "decode.d3.loss_mask": 0.030956038273870945, "decode.d3.loss_dice": 0.04589415639638901, "decode.d4.loss_cls": 0.014555977303825785, "decode.d4.loss_mask": 0.030475340597331525, "decode.d4.loss_dice": 0.04581968560814857, "time": 0.26160833835601804, "iter": 10000, "memory": 2624, "step": 10000} +{"aAcc": 98.96, "mIoU": 77.5, "mAcc": 87.45, "data_time": 0.001611786730149213, "time": 0.04820781244951136, "step": 10000} diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/config.py b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/config.py new file mode 100644 index 0000000000..2d10c53a59 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/config.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/scalars.json new file mode 100644 index 0000000000..0ededb5788 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 0.005614161491394043, "grad_norm": 518.7013122558594, "loss": 22.32619705200195, "decode.loss_cls": 3.158992862701416, "decode.loss_mask": 0.20419039502739905, "decode.loss_dice": 0.4701965257525444, "decode.d0.loss_cls": 2.833039093017578, "decode.d0.loss_mask": 0.19366986453533172, "decode.d0.loss_dice": 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"mAcc": 87.45, "data_time": 0.001611786730149213, "time": 0.04820781244951136, "step": 10000} diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512.py new file mode 100644 index 0000000000..2d10c53a59 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_122327/vis_data/20241107_122327.json b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_122327/vis_data/20241107_122327.json new file mode 100644 index 0000000000..c47556136f --- /dev/null +++ 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b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_122327/vis_data/config.py new file mode 100644 index 0000000000..0e1b1519e4 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_122327/vis_data/config.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 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0.018054110162398395, "step": 10000} diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/segformer_mit-b0_1xb2-10k_arid10_cat-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/segformer_mit-b0_1xb2-10k_arid10_cat-512x512.py new file mode 100644 index 0000000000..0e1b1519e4 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/segformer_mit-b0_1xb2-10k_arid10_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/20241107_124022.json b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/20241107_124022.json new file mode 100644 index 0000000000..8fe9a66cbd --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/20241107_124022.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 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+{"base_lr": 1.2001600213361763e-06, "lr": 1.2001600213361763e-06, "data_time": 0.00472872257232666, "loss": 0.022800351586192848, "decode.loss_ce": 0.022800351586192848, "decode.acc_seg": 99.19605255126953, "time": 0.14266884326934814, "iter": 9850, "memory": 1344, "step": 9850} +{"base_lr": 8.001066808907855e-07, "lr": 8.001066808907855e-07, "data_time": 0.0046165227890014645, "loss": 0.02459447328001261, "decode.loss_ce": 0.02459447328001261, "decode.acc_seg": 99.06686401367188, "time": 0.1423332691192627, "iter": 9900, "memory": 1344, "step": 9900} +{"base_lr": 4.000533404453931e-07, "lr": 4.000533404453931e-07, "data_time": 0.004556989669799805, "loss": 0.021980740129947662, "decode.loss_ce": 0.021980740129947662, "decode.acc_seg": 98.60458374023438, "time": 0.1423478126525879, "iter": 9950, "memory": 1344, "step": 9950} +{"base_lr": 0.0, "lr": 0.0, "data_time": 0.004446268081665039, "loss": 0.03264338420704007, "decode.loss_ce": 0.03264338420704007, "decode.acc_seg": 99.69121551513672, "time": 0.14220120906829833, "iter": 10000, "memory": 1344, "step": 10000} +{"aAcc": 98.88, "mIoU": 76.15, "mAcc": 83.11, "data_time": 0.0014969846781562356, "time": 0.02160899428760304, "step": 10000} diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/config.py b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/config.py new file mode 100644 index 0000000000..f3edc434db --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/config.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/scalars.json new file mode 100644 index 0000000000..8fe9a66cbd --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.005095911026000976, "loss": 2.9879141569137575, "decode.loss_ce": 2.9879141569137575, "decode.acc_seg": 0.021314797922968864, "time": 0.15326998233795167, "iter": 50, "memory": 3230, "step": 50} +{"base_lr": 3.962697798532355e-06, "lr": 3.962697798532355e-06, "data_time": 0.004787039756774902, "loss": 2.8165255308151247, "decode.loss_ce": 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a/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512.py new file mode 100644 index 0000000000..f3edc434db --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20240917_114220/vis_data/20240917_114220.json b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20240917_114220/vis_data/20240917_114220.json new file mode 100644 index 0000000000..8708068d5d --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20240917_114220/vis_data/20240917_114220.json @@ -0,0 +1,210 @@ +{"lr": 0.0006470980653769185, "data_time": 0.0023443222045898436, "loss": 3.194075566530228, "decode.loss_ce": 1.0545195966959, "decode.acc_seg": 58.623165130615234, "aux_0.loss_ce": 1.0397097498178482, "aux_0.acc_seg": 58.688602447509766, "aux_1.loss_ce": 1.0998461604118348, "aux_1.acc_seg": 58.62216567993164, "time": 0.09662184715270997, "iter": 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+ size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20240917_114220/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20240917_114220/vis_data/scalars.json new file mode 100644 index 0000000000..8708068d5d --- /dev/null +++ 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a/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..bff6f6bba1 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_161558/vis_data/20240917_161558.json b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_161558/vis_data/20240917_161558.json new file mode 100644 index 0000000000..b19363571b --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_161558/vis_data/20240917_161558.json @@ -0,0 +1,210 @@ +{"base_lr": 9.955884763429715e-05, "lr": 9.955884763429715e-06, "data_time": 0.005344867706298828, "grad_norm": 194.01115036010742, "loss": 26.501869201660156, "decode.loss_cls": 0.829374247789383, "decode.loss_mask": 0.42971041798591614, "decode.loss_dice": 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imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_161558/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_161558/vis_data/scalars.json new file mode 100644 index 0000000000..b19363571b --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_161558/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 9.955884763429715e-05, "lr": 9.955884763429715e-06, "data_time": 0.005344867706298828, "grad_norm": 194.01115036010742, "loss": 26.501869201660156, "decode.loss_cls": 0.829374247789383, "decode.loss_mask": 0.42971041798591614, "decode.loss_dice": 0.6105233907699585, "decode.d0.loss_cls": 6.766006660461426, "decode.d0.loss_mask": 0.6562121897935868, "decode.d0.loss_dice": 1.0857217490673066, "decode.d1.loss_cls": 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pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/20240917_172715.json b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/20240917_172715.json new file mode 100644 index 0000000000..198798d077 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/20240917_172715.json @@ -0,0 +1,210 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 0.0051671028137207035, "grad_norm": 470.30755310058595, "loss": 23.879179000854492, "decode.loss_cls": 3.3874343633651733, "decode.loss_mask": 0.17753420174121856, "decode.loss_dice": 0.3346044570207596, "decode.d0.loss_cls": 3.4928240299224855, "decode.d0.loss_mask": 0.17159825190901756, 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b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/config.py new file mode 100644 index 0000000000..df7a8dc4f0 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/config.py @@ -0,0 +1,525 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/scalars.json new file mode 100644 index 0000000000..198798d077 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 0.0051671028137207035, "grad_norm": 470.30755310058595, "loss": 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+{"aAcc": 98.95, "mIoU": 87.52, "mAcc": 93.06, "data_time": 0.0014193153381347656, "time": 0.04673076629638672, "step": 10000} diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..e07ae267ce --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/20240917_120056/vis_data/20240917_120056.json b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/20240917_120056/vis_data/20240917_120056.json new file mode 100644 index 0000000000..bb247aadd2 --- /dev/null +++ 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b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/20240917_120056/vis_data/config.py @@ -0,0 +1,322 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/20240917_120056/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/20240917_120056/vis_data/scalars.json new file mode 100644 index 0000000000..bb247aadd2 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/20240917_120056/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.0033666133880615235, "loss": 3.074103283882141, "decode.loss_ce": 3.074103283882141, "decode.acc_seg": 8.683829307556152, "time": 0.09878559112548828, "iter": 50, "memory": 2068, "step": 50} +{"base_lr": 3.962697798532355e-06, "lr": 3.962697798532355e-06, "data_time": 0.0033510446548461912, "loss": 2.983224391937256, "decode.loss_ce": 2.983224391937256, "decode.acc_seg": 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a/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..0d9ad30ef8 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/20240917_121739.json b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/20240917_121739.json new file mode 100644 index 0000000000..cc70a96776 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/20240917_121739.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.004879283905029297, "loss": 3.111197996139526, "decode.loss_ce": 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"data_time": 0.0014802050590515137, "time": 0.023037610054016115, "step": 10000} diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/config.py b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/config.py new file mode 100644 index 0000000000..a8f9dbb8fa --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/config.py @@ -0,0 +1,344 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/scalars.json new file mode 100644 index 0000000000..cc70a96776 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.004879283905029297, "loss": 3.111197996139526, "decode.loss_ce": 3.111197996139526, "decode.acc_seg": 58.2942008972168, "time": 0.15078768730163575, "iter": 50, "memory": 3230, "step": 50} +{"base_lr": 3.962697798532355e-06, "lr": 3.962697798532355e-06, "data_time": 0.004775619506835938, "loss": 2.777503323554993, "decode.loss_ce": 2.777503323554993, 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a/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..344a86a610 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/20240805_022137/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/20240805_022137/vis_data/scalars.json new file mode 100644 index 0000000000..cf34e7d0a9 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/20240805_022137/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 21.96} +{"step": 2000, "mIoU": 49.14} +{"step": 3000, "mIoU": 60.57} +{"step": 4000, "mIoU": 64.15} +{"step": 5000, "mIoU": 65.96} +{"step": 6000, "mIoU": 70.07} +{"step": 7000, "mIoU": 71.17} +{"step": 8000, "mIoU": 72.4} +{"step": 9000, "mIoU": 75.0} +{"step": 10000, "mIoU": 76.82} diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..c1210fe31e --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_031129/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_031129/vis_data/scalars.json new file mode 100644 index 0000000000..f13abf5355 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_031129/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 65.54} +{"step": 2000, "mIoU": 70.33} +{"step": 3000, "mIoU": 77.32} +{"step": 4000, "mIoU": 79.43} +{"step": 5000, "mIoU": 81.82} +{"step": 6000, "mIoU": 83.13} +{"step": 7000, "mIoU": 85.55} +{"step": 8000, "mIoU": 79.95} +{"step": 9000, "mIoU": 84.92} +{"step": 10000, "mIoU": 82.69} diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..aac4b92191 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512.py @@ -0,0 +1,447 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_052833/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_052833/vis_data/scalars.json new file mode 100644 index 0000000000..0c7d61fbe9 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_052833/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 6.35} +{"step": 2000, "mIoU": 30.41} +{"step": 3000, "mIoU": 50.1} +{"step": 4000, "mIoU": 67.93} +{"step": 5000, "mIoU": 69.17} +{"step": 6000, "mIoU": 74.48} +{"step": 7000, "mIoU": 77.79} +{"step": 8000, "mIoU": 80.74} +{"step": 9000, "mIoU": 81.36} +{"step": 10000, "mIoU": 78.16} diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..4a1e0f9c65 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512.py @@ -0,0 +1,537 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/20240805_065747/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/20240805_065747/vis_data/scalars.json new file mode 100644 index 0000000000..168b26a328 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/20240805_065747/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 27.4} +{"step": 2000, "mIoU": 66.87} +{"step": 3000, "mIoU": 72.3} +{"step": 4000, "mIoU": 75.33} +{"step": 5000, "mIoU": 75.85} +{"step": 6000, "mIoU": 75.37} +{"step": 7000, "mIoU": 77.84} +{"step": 8000, "mIoU": 78.78} +{"step": 9000, "mIoU": 79.08} +{"step": 10000, "mIoU": 78.79} diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_mit-b0_1xb2-10k_hots-v1-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_mit-b0_1xb2-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..3894a9cd63 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_mit-b0_1xb2-10k_hots-v1-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_hots-v1-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_073545/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_073545/vis_data/scalars.json new file mode 100644 index 0000000000..25dd462849 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_073545/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 24.39} +{"step": 2000, "mIoU": 67.3} +{"step": 3000, "mIoU": 75.59} +{"step": 4000, "mIoU": 78.33} +{"step": 5000, "mIoU": 80.14} +{"step": 6000, "mIoU": 80.8} +{"step": 7000, "mIoU": 84.13} +{"step": 8000, "mIoU": 83.16} +{"step": 9000, "mIoU": 82.34} +{"step": 10000, "mIoU": 81.75} diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..8d3bb82ac6 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=47, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/20240920_124652.json b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/20240920_124652.json new file mode 100644 index 0000000000..06c9db4fe7 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/20240920_124652.json @@ -0,0 +1,211 @@ +{"lr": 0.0006200800533689131, "data_time": 0.002659726142883301, "loss": 2.9682978868484495, "decode.loss_ce": 0.9420152902603149, "decode.acc_seg": 78.80119323730469, "aux_0.loss_ce": 1.0499185621738434, "aux_0.acc_seg": 78.80119323730469, "aux_1.loss_ce": 0.9763640403747559, "aux_1.acc_seg": 78.80119323730469, "time": 0.09340112209320069, 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a/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/config.py b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/config.py new file mode 100644 index 0000000000..5277c4bd18 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/config.py @@ -0,0 +1,335 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 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a/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512.py new file mode 100644 index 0000000000..5277c4bd18 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512.py @@ -0,0 +1,335 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/20240920_130500.json b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/20240920_130500.json new file mode 100644 index 0000000000..b4dc23be97 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/20240920_130500.json @@ -0,0 +1,211 @@ +{"base_lr": 9.963989187614348e-05, "lr": 9.963989187614342e-06, "data_time": 0.0056323051452636715, "grad_norm": 205.09661560058595, "loss": 22.58833866119385, "decode.loss_cls": 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b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/config.py new file mode 100644 index 0000000000..5dc6472aa4 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/config.py @@ -0,0 +1,428 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/scalars.json new file mode 100644 index 0000000000..b4dc23be97 --- /dev/null +++ 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0.07924876213073731, "step": 10000} diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py new file mode 100644 index 0000000000..5dc6472aa4 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py @@ -0,0 +1,428 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/20240925_130957.json b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/20240925_130957.json new file mode 100644 index 0000000000..e543e6b8c1 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/20240925_130957.json @@ -0,0 +1,211 @@ +{"base_lr": 4.801555391078211e-07, "lr": 4.801555391078211e-07, "data_time": 0.004893159866333008, "grad_norm": 487.06109924316405, "loss": 26.960327339172363, "decode.loss_cls": 3.944627380371094, "decode.loss_mask": 0.13833609782159328, "decode.loss_dice": 0.38492500483989717, "decode.d0.loss_cls": 4.054897451400757, "decode.d0.loss_mask": 0.1375544723123312, 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"decode.d2.loss_cls": 0.005539317475631833, "decode.d2.loss_mask": 0.01890755109488964, "decode.d2.loss_dice": 0.04066711440682411, "decode.d3.loss_cls": 0.005642322232597507, "decode.d3.loss_mask": 0.01891419608145952, "decode.d3.loss_dice": 0.04083530604839325, "decode.d4.loss_cls": 0.005611454816244077, "decode.d4.loss_mask": 0.018657590076327325, "decode.d4.loss_dice": 0.04117581769824028, "time": 0.26025140285491943, "iter": 10000, "memory": 2807, "step": 10000} +{"aAcc": 98.96, "mIoU": 88.44, "mAcc": 92.73, "data_time": 0.002820611000061035, "time": 0.04976010322570801, "step": 10000} diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/config.py b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/config.py new file mode 100644 index 0000000000..585dc791fb --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/config.py @@ -0,0 +1,518 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/scalars.json new file mode 100644 index 0000000000..e543e6b8c1 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/scalars.json @@ -0,0 +1,211 @@ +{"base_lr": 4.801555391078211e-07, "lr": 4.801555391078211e-07, "data_time": 0.004893159866333008, "grad_norm": 487.06109924316405, "loss": 26.960327339172363, "decode.loss_cls": 3.944627380371094, "decode.loss_mask": 0.13833609782159328, "decode.loss_dice": 0.38492500483989717, "decode.d0.loss_cls": 4.054897451400757, "decode.d0.loss_mask": 0.1375544723123312, "decode.d0.loss_dice": 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b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py new file mode 100644 index 0000000000..585dc791fb --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py @@ -0,0 +1,518 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240920_141808/vis_data/20240920_141808.json b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240920_141808/vis_data/20240920_141808.json new file mode 100644 index 0000000000..b73514dfc9 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240920_141808/vis_data/20240920_141808.json @@ -0,0 +1,211 @@ +{"base_lr": 1.6011257771847904e-06, "lr": 1.6011257771847904e-06, "data_time": 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b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240920_141808/vis_data/config.py new file mode 100644 index 0000000000..98319fec51 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240920_141808/vis_data/config.py @@ -0,0 +1,321 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240920_141808/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240920_141808/vis_data/scalars.json new file mode 100644 index 0000000000..b73514dfc9 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240920_141808/vis_data/scalars.json @@ -0,0 +1,211 @@ +{"base_lr": 1.6011257771847904e-06, "lr": 1.6011257771847904e-06, "data_time": 0.003428483009338379, "loss": 3.0422013998031616, "decode.loss_ce": 3.0422013998031616, "decode.acc_seg": 8.04440975189209, "time": 0.10466759204864502, "iter": 41, "memory": 1871, "step": 41} 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b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512.py @@ -0,0 +1,321 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_143546/vis_data/20240920_143546.json b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_143546/vis_data/20240920_143546.json new file mode 100644 index 0000000000..bd039afb70 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_143546/vis_data/20240920_143546.json @@ -0,0 +1,211 @@ +{"base_lr": 1.6011257771847904e-06, "lr": 1.6011257771847904e-06, "data_time": 0.00507822036743164, "loss": 2.775026488304138, "decode.loss_ce": 2.775026488304138, "decode.acc_seg": 8.453996658325195, "time": 0.1579904317855835, "iter": 41, "memory": 3228, "step": 41} +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.004999637603759766, "loss": 2.919499945640564, 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"time": 0.144608473777771, "iter": 10000, "memory": 1308, "step": 10000} +{"aAcc": 98.9, "mIoU": 88.42, "mAcc": 93.12, "data_time": 0.0023869752883911135, "time": 0.022557401657104494, "step": 10000} diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_143546/vis_data/config.py b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_143546/vis_data/config.py new file mode 100644 index 0000000000..be08b5a6df --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_143546/vis_data/config.py @@ -0,0 +1,343 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=26, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_143546/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_143546/vis_data/scalars.json new file mode 100644 index 0000000000..bd039afb70 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_143546/vis_data/scalars.json @@ -0,0 +1,211 @@ +{"base_lr": 1.6011257771847904e-06, "lr": 1.6011257771847904e-06, "data_time": 0.00507822036743164, "loss": 2.775026488304138, "decode.loss_ce": 2.775026488304138, "decode.acc_seg": 8.453996658325195, "time": 0.1579904317855835, "iter": 41, "memory": 3228, "step": 41} +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.004999637603759766, "loss": 2.919499945640564, "decode.loss_ce": 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1.2001600213361763e-06, "lr": 1.2001600213361763e-06, "data_time": 0.0047750711441040036, "loss": 0.02297932580113411, "decode.loss_ce": 0.02297932580113411, "decode.acc_seg": 98.76346588134766, "time": 0.14946911334991456, "iter": 9850, "memory": 1308, "step": 9850} +{"base_lr": 8.001066808907855e-07, "lr": 8.001066808907855e-07, "data_time": 0.00482645034790039, "loss": 0.018679415434598924, "decode.loss_ce": 0.018679415434598924, "decode.acc_seg": 98.59180450439453, "time": 0.15215177536010743, "iter": 9900, "memory": 1308, "step": 9900} +{"base_lr": 4.000533404453931e-07, "lr": 4.000533404453931e-07, "data_time": 0.0047910213470458984, "loss": 0.020692188385874034, "decode.loss_ce": 0.020692188385874034, "decode.acc_seg": 98.72303009033203, "time": 0.15138788223266603, "iter": 9950, "memory": 1308, "step": 9950} +{"base_lr": 0.0, "lr": 0.0, "data_time": 0.004697990417480469, "loss": 0.023846511729061604, "decode.loss_ce": 0.023846511729061604, "decode.acc_seg": 99.15471649169922, "time": 0.144608473777771, "iter": 10000, "memory": 1308, "step": 10000} +{"aAcc": 98.9, "mIoU": 88.42, "mAcc": 93.12, "data_time": 0.0023869752883911135, "time": 0.022557401657104494, "step": 10000} diff --git a/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py new file mode 100644 index 0000000000..be08b5a6df --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py @@ -0,0 +1,343 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=26, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/20240805_024317/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/20240805_024317/vis_data/scalars.json new file mode 100644 index 0000000000..64d9fb8339 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/20240805_024317/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 1.97} +{"step": 2000, "mIoU": 19.79} +{"step": 3000, "mIoU": 40.13} +{"step": 4000, "mIoU": 51.99} +{"step": 5000, "mIoU": 63.36} +{"step": 6000, "mIoU": 68.4} +{"step": 7000, "mIoU": 68.55} +{"step": 8000, "mIoU": 70.28} +{"step": 9000, "mIoU": 72.84} +{"step": 10000, "mIoU": 72.8} diff --git a/my_projects/best_models/selection_trained/fine_tuning/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..8b4fa27721 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512.py @@ -0,0 +1,336 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_042040/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_042040/vis_data/scalars.json new file mode 100644 index 0000000000..9d5cbec6e2 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_042040/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 47.08} +{"step": 2000, "mIoU": 73.71} +{"step": 3000, "mIoU": 86.34} +{"step": 4000, "mIoU": 85.83} +{"step": 5000, "mIoU": 83.01} +{"step": 6000, "mIoU": 84.03} +{"step": 7000, "mIoU": 85.76} +{"step": 8000, "mIoU": 88.94} +{"step": 9000, "mIoU": 90.91} +{"step": 10000, "mIoU": 88.91} diff --git a/my_projects/best_models/selection_trained/fine_tuning/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..103405e14f --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py @@ -0,0 +1,475 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_061403/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_061403/vis_data/scalars.json new file mode 100644 index 0000000000..3f3699e26d --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_061403/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 1.66} +{"step": 2000, "mIoU": 14.13} +{"step": 3000, "mIoU": 25.94} +{"step": 4000, "mIoU": 47.14} +{"step": 5000, "mIoU": 62.73} +{"step": 6000, "mIoU": 73.38} +{"step": 7000, "mIoU": 80.31} +{"step": 8000, "mIoU": 82.67} +{"step": 9000, "mIoU": 83.12} +{"step": 10000, "mIoU": 83.92} diff --git a/my_projects/best_models/selection_trained/fine_tuning/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..f38140688b --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py @@ -0,0 +1,565 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/20240805_071527/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/20240805_071527/vis_data/scalars.json new file mode 100644 index 0000000000..ca7274c96e --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/20240805_071527/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 4.11} +{"step": 2000, "mIoU": 55.63} +{"step": 3000, "mIoU": 67.65} +{"step": 4000, "mIoU": 78.75} +{"step": 5000, "mIoU": 82.38} +{"step": 6000, "mIoU": 82.57} +{"step": 7000, "mIoU": 84.88} +{"step": 8000, "mIoU": 83.25} +{"step": 9000, "mIoU": 84.23} +{"step": 10000, "mIoU": 85.13} diff --git a/my_projects/best_models/selection_trained/fine_tuning/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..7c56d446e5 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512.py @@ -0,0 +1,322 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_080116/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_080116/vis_data/scalars.json new file mode 100644 index 0000000000..c676752b87 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_080116/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 5.71} +{"step": 2000, "mIoU": 50.8} +{"step": 3000, "mIoU": 70.42} +{"step": 4000, "mIoU": 80.92} +{"step": 5000, "mIoU": 82.79} +{"step": 6000, "mIoU": 85.41} +{"step": 7000, "mIoU": 85.65} +{"step": 8000, "mIoU": 87.74} +{"step": 9000, "mIoU": 87.87} +{"step": 10000, "mIoU": 88.97} diff --git a/my_projects/best_models/selection_trained/fine_tuning/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..5d8bf45d9c --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py @@ -0,0 +1,344 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=72, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/20240924_110746.json b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/20240924_110746.json new file mode 100644 index 0000000000..1ed4f0a39f --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/20240924_110746.json @@ -0,0 +1,210 @@ +{"lr": 0.0006470980653769185, "data_time": 0.002445840835571289, "loss": 1.4245377123355865, "decode.loss_ce": 0.460920050740242, "decode.acc_seg": 96.78573608398438, "aux_0.loss_ce": 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b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/config.py new file mode 100644 index 0000000000..e3436abb10 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/config.py @@ -0,0 +1,336 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/scalars.json new file mode 100644 index 0000000000..1ed4f0a39f --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"lr": 0.0006470980653769185, "data_time": 0.002445840835571289, "loss": 1.4245377123355865, "decode.loss_ce": 0.460920050740242, "decode.acc_seg": 96.78573608398438, "aux_0.loss_ce": 0.43786360919475553, "aux_0.acc_seg": 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b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..e3436abb10 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,336 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_112623/vis_data/20240924_112623.json b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_112623/vis_data/20240924_112623.json new file mode 100644 index 0000000000..97a38b5081 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_112623/vis_data/20240924_112623.json @@ -0,0 +1,210 @@ +{"base_lr": 9.955884763429715e-05, "lr": 9.955884763429715e-06, "data_time": 0.00566253662109375, "grad_norm": 92.14458770751953, "loss": 12.631502532958985, "decode.loss_cls": 0.34186268597841263, "decode.loss_mask": 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"mIoU": 87.5, "mAcc": 92.16, "data_time": 0.0017285744349161785, "time": 0.07100030581156412, "step": 10000} diff --git a/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_112623/vis_data/config.py b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_112623/vis_data/config.py new file mode 100644 index 0000000000..d802256f03 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_112623/vis_data/config.py @@ -0,0 +1,433 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_112623/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_112623/vis_data/scalars.json new file mode 100644 index 0000000000..97a38b5081 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_112623/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 9.955884763429715e-05, "lr": 9.955884763429715e-06, "data_time": 0.00566253662109375, "grad_norm": 92.14458770751953, "loss": 12.631502532958985, "decode.loss_cls": 0.34186268597841263, "decode.loss_mask": 0.0973613753914833, "decode.loss_dice": 0.1479683645069599, "decode.d0.loss_cls": 6.51755952835083, "decode.d0.loss_mask": 0.14761312752962114, "decode.d0.loss_dice": 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a/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..d802256f03 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,433 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_124010/vis_data/20240924_124010.json b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_124010/vis_data/20240924_124010.json new file mode 100644 index 0000000000..00e6bb52ec --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_124010/vis_data/20240924_124010.json @@ -0,0 +1,210 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 0.004952335357666015, "grad_norm": 589.998159790039, "loss": 23.99028034210205, "decode.loss_cls": 3.7930330514907835, "decode.loss_mask": 0.11582119278609752, "decode.loss_dice": 0.20272027775645257, "decode.d0.loss_cls": 3.5208041667938232, 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b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_124010/vis_data/config.py @@ -0,0 +1,523 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 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b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..28082765fa --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,523 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/20240924_132330.json b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/20240924_132330.json new file mode 100644 index 0000000000..f601f3a3c6 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/20240924_132330.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.003247380256652832, "loss": 3.061506676673889, "decode.loss_ce": 3.061506676673889, "decode.acc_seg": 14.821052551269531, "time": 0.0987546682357788, "iter": 50, "memory": 2002, "step": 50} +{"base_lr": 3.962697798532355e-06, "lr": 3.962697798532355e-06, "data_time": 0.003378415107727051, "loss": 2.655226707458496, "decode.loss_ce": 2.655226707458496, "decode.acc_seg": 91.31123352050781, "time": 0.09768564701080322, "iter": 100, "memory": 1092, "step": 100} +{"base_lr": 5.964030020013346e-06, "lr": 5.964030020013346e-06, "data_time": 0.0033515214920043944, "loss": 2.4124140977859496, "decode.loss_ce": 2.4124140977859496, "decode.acc_seg": 93.06529998779297, "time": 0.09537525177001953, "iter": 150, "memory": 1092, 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b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/config.py new file mode 100644 index 0000000000..9268b6dc88 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/config.py @@ -0,0 +1,322 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/scalars.json new file mode 100644 index 0000000000..f601f3a3c6 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.003247380256652832, "loss": 3.061506676673889, "decode.loss_ce": 3.061506676673889, 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"iter": 9950, "memory": 1092, "step": 9950} +{"base_lr": 0.0, "lr": 0.0, "data_time": 0.003294682502746582, "loss": 0.00769683497492224, "decode.loss_ce": 0.00769683497492224, "decode.acc_seg": 99.67753601074219, "time": 0.09499402046203613, "iter": 10000, "memory": 1092, "step": 10000} +{"aAcc": 99.62, "mIoU": 87.51, "mAcc": 94.92, "data_time": 0.0015500783920288086, "time": 0.01838030815124512, "step": 10000} diff --git a/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..9268b6dc88 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,322 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 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1334, "step": 10000} +{"aAcc": 99.72, "mIoU": 89.91, "mAcc": 94.83, "data_time": 0.0015585581461588542, "time": 0.0221388578414917, "step": 10000} diff --git a/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_133957/vis_data/config.py b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_133957/vis_data/config.py new file mode 100644 index 0000000000..b52e5d405d --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_133957/vis_data/config.py @@ -0,0 +1,344 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=30, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_133957/vis_data/scalars.json b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_133957/vis_data/scalars.json new file mode 100644 index 0000000000..7dea46ef02 --- /dev/null +++ b/my_projects/best_models/selection_trained/fine_tuning/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_133957/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.004604220390319824, "loss": 2.9579598188400267, "decode.loss_ce": 2.9579598188400267, "decode.acc_seg": 5.171775817871094, "time": 0.1445002555847168, "iter": 50, "memory": 3232, "step": 50} +{"base_lr": 3.962697798532355e-06, 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+data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=30, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/20240805_022137/vis_data/scalars.json b/my_projects/best_models/selection_trained/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/20240805_022137/vis_data/scalars.json new file mode 100644 index 0000000000..cf34e7d0a9 --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/20240805_022137/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 21.96} +{"step": 2000, "mIoU": 49.14} +{"step": 3000, "mIoU": 60.57} +{"step": 4000, "mIoU": 64.15} +{"step": 5000, "mIoU": 65.96} +{"step": 6000, "mIoU": 70.07} +{"step": 7000, "mIoU": 71.17} +{"step": 8000, "mIoU": 72.4} +{"step": 9000, "mIoU": 75.0} +{"step": 10000, "mIoU": 76.82} diff --git a/my_projects/best_models/selection_trained/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512.py b/my_projects/best_models/selection_trained/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..c1210fe31e --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512' diff --git a/my_projects/best_models/selection_trained/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_031129/vis_data/scalars.json b/my_projects/best_models/selection_trained/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_031129/vis_data/scalars.json new file mode 100644 index 0000000000..f13abf5355 --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_031129/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 65.54} +{"step": 2000, "mIoU": 70.33} +{"step": 3000, "mIoU": 77.32} +{"step": 4000, "mIoU": 79.43} +{"step": 5000, "mIoU": 81.82} +{"step": 6000, "mIoU": 83.13} +{"step": 7000, "mIoU": 85.55} +{"step": 8000, "mIoU": 79.95} +{"step": 9000, "mIoU": 84.92} +{"step": 10000, "mIoU": 82.69} diff --git a/my_projects/best_models/selection_trained/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512.py b/my_projects/best_models/selection_trained/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..aac4b92191 --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512.py @@ -0,0 +1,447 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/best_models/selection_trained/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_052833/vis_data/scalars.json b/my_projects/best_models/selection_trained/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_052833/vis_data/scalars.json new file mode 100644 index 0000000000..0c7d61fbe9 --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_052833/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 6.35} +{"step": 2000, "mIoU": 30.41} +{"step": 3000, "mIoU": 50.1} +{"step": 4000, "mIoU": 67.93} +{"step": 5000, "mIoU": 69.17} +{"step": 6000, "mIoU": 74.48} +{"step": 7000, "mIoU": 77.79} +{"step": 8000, "mIoU": 80.74} +{"step": 9000, "mIoU": 81.36} +{"step": 10000, "mIoU": 78.16} diff --git a/my_projects/best_models/selection_trained/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512.py b/my_projects/best_models/selection_trained/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..4a1e0f9c65 --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512.py @@ -0,0 +1,537 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/best_models/selection_trained/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/20240805_065747/vis_data/scalars.json b/my_projects/best_models/selection_trained/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/20240805_065747/vis_data/scalars.json new file mode 100644 index 0000000000..168b26a328 --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/20240805_065747/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 27.4} +{"step": 2000, "mIoU": 66.87} +{"step": 3000, "mIoU": 72.3} +{"step": 4000, "mIoU": 75.33} +{"step": 5000, "mIoU": 75.85} +{"step": 6000, "mIoU": 75.37} +{"step": 7000, "mIoU": 77.84} +{"step": 8000, "mIoU": 78.78} +{"step": 9000, "mIoU": 79.08} +{"step": 10000, "mIoU": 78.79} diff --git a/my_projects/best_models/selection_trained/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_mit-b0_1xb2-10k_hots-v1-512x512.py b/my_projects/best_models/selection_trained/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_mit-b0_1xb2-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..3894a9cd63 --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_mit-b0_1xb2-10k_hots-v1-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_hots-v1-512x512' diff --git a/my_projects/best_models/selection_trained/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_073545/vis_data/scalars.json b/my_projects/best_models/selection_trained/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_073545/vis_data/scalars.json new file mode 100644 index 0000000000..25dd462849 --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_073545/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 24.39} +{"step": 2000, "mIoU": 67.3} +{"step": 3000, "mIoU": 75.59} +{"step": 4000, "mIoU": 78.33} +{"step": 5000, "mIoU": 80.14} +{"step": 6000, "mIoU": 80.8} +{"step": 7000, "mIoU": 84.13} +{"step": 8000, "mIoU": 83.16} +{"step": 9000, "mIoU": 82.34} +{"step": 10000, "mIoU": 81.75} diff --git a/my_projects/best_models/selection_trained/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512.py b/my_projects/best_models/selection_trained/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..8d3bb82ac6 --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=47, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/best_models/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/20240920_124652.json b/my_projects/best_models/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/20240920_124652.json new file mode 100644 index 0000000000..06c9db4fe7 --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/20240920_124652.json @@ -0,0 +1,211 @@ +{"lr": 0.0006200800533689131, "data_time": 0.002659726142883301, "loss": 2.9682978868484495, "decode.loss_ce": 0.9420152902603149, "decode.acc_seg": 78.80119323730469, "aux_0.loss_ce": 1.0499185621738434, "aux_0.acc_seg": 78.80119323730469, "aux_1.loss_ce": 0.9763640403747559, "aux_1.acc_seg": 78.80119323730469, "time": 0.09340112209320069, "iter": 41, "memory": 2549, "step": 41} +{"lr": 0.0006470980653769185, "data_time": 0.002815699577331543, "loss": 2.729179787635803, "decode.loss_ce": 0.8691318392753601, "decode.acc_seg": 77.79108428955078, "aux_0.loss_ce": 0.9739946186542511, "aux_0.acc_seg": 77.79108428955078, "aux_1.loss_ce": 0.886053329706192, "aux_1.acc_seg": 77.79108428955078, "time": 0.09533278942108155, "iter": 50, "memory": 774, "step": 50} 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"decode.loss_ce": 0.027609426714479923, "decode.acc_seg": 99.0223617553711, "aux_0.loss_ce": 0.029689188301563262, "aux_0.acc_seg": 98.63114166259766, "aux_1.loss_ce": 0.043169558979570866, "aux_1.acc_seg": 98.2076644897461, "time": 0.10427672863006592, "iter": 10000, "memory": 774, "step": 10000} +{"aAcc": 98.22, "mIoU": 80.5, "mAcc": 87.88, "data_time": 0.0023630619049072265, "time": 0.015045881271362305, "step": 10000} diff --git a/my_projects/best_models/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/config.py b/my_projects/best_models/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/config.py new file mode 100644 index 0000000000..5277c4bd18 --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/config.py @@ -0,0 +1,335 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/scalars.json b/my_projects/best_models/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/scalars.json new file mode 100644 index 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b/my_projects/best_models/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512.py @@ -0,0 +1,335 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/20240920_130500.json 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0.4434641122817993, "iter": 10000, "memory": 2950, "step": 10000} +{"aAcc": 98.98, "mIoU": 90.04, "mAcc": 93.6, "data_time": 0.0025933504104614256, "time": 0.07924876213073731, "step": 10000} diff --git a/my_projects/best_models/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/config.py b/my_projects/best_models/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/config.py new file mode 100644 index 0000000000..5dc6472aa4 --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/config.py @@ -0,0 +1,428 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/scalars.json b/my_projects/best_models/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/scalars.json new file mode 100644 index 0000000000..b4dc23be97 --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/scalars.json @@ -0,0 +1,211 @@ +{"base_lr": 9.963989187614348e-05, "lr": 9.963989187614342e-06, "data_time": 0.0056323051452636715, "grad_norm": 205.09661560058595, "loss": 22.58833866119385, "decode.loss_cls": 0.585413932800293, "decode.loss_mask": 0.4014120578765869, "decode.loss_dice": 0.5713946372270584, "decode.d0.loss_cls": 6.894965267181396, "decode.d0.loss_mask": 0.49705882370471954, "decode.d0.loss_dice": 0.8819123923778533, "decode.d1.loss_cls": 0.6922816574573517, 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b/my_projects/best_models/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py @@ -0,0 +1,428 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/20240925_130957.json b/my_projects/best_models/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/20240925_130957.json new file mode 100644 index 0000000000..e543e6b8c1 --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/20240925_130957.json @@ -0,0 +1,211 @@ +{"base_lr": 4.801555391078211e-07, "lr": 4.801555391078211e-07, "data_time": 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"decode.loss_dice": 0.04062049593776464, "decode.d0.loss_cls": 0.007335875289572869, "decode.d0.loss_mask": 0.019258128013461828, "decode.d0.loss_dice": 0.04081912934780121, "decode.d1.loss_cls": 0.0062700475886231285, "decode.d1.loss_mask": 0.018930184468626977, "decode.d1.loss_dice": 0.04057639706879854, "decode.d2.loss_cls": 0.005539317475631833, "decode.d2.loss_mask": 0.01890755109488964, "decode.d2.loss_dice": 0.04066711440682411, "decode.d3.loss_cls": 0.005642322232597507, "decode.d3.loss_mask": 0.01891419608145952, "decode.d3.loss_dice": 0.04083530604839325, "decode.d4.loss_cls": 0.005611454816244077, "decode.d4.loss_mask": 0.018657590076327325, "decode.d4.loss_dice": 0.04117581769824028, "time": 0.26025140285491943, "iter": 10000, "memory": 2807, "step": 10000} +{"aAcc": 98.96, "mIoU": 88.44, "mAcc": 92.73, "data_time": 0.002820611000061035, "time": 0.04976010322570801, "step": 10000} diff --git a/my_projects/best_models/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/config.py b/my_projects/best_models/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/config.py new file mode 100644 index 0000000000..585dc791fb --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/config.py @@ -0,0 +1,518 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/scalars.json b/my_projects/best_models/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/scalars.json new file mode 100644 index 0000000000..e543e6b8c1 --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/scalars.json @@ -0,0 +1,211 @@ +{"base_lr": 4.801555391078211e-07, "lr": 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a/my_projects/best_models/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py b/my_projects/best_models/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py new file mode 100644 index 0000000000..585dc791fb --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py @@ -0,0 +1,518 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240920_141808/vis_data/20240920_141808.json b/my_projects/best_models/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240920_141808/vis_data/20240920_141808.json new file mode 100644 index 0000000000..b73514dfc9 --- /dev/null +++ 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b/my_projects/best_models/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240920_141808/vis_data/config.py new file mode 100644 index 0000000000..98319fec51 --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240920_141808/vis_data/config.py @@ -0,0 +1,321 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240920_141808/vis_data/scalars.json b/my_projects/best_models/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240920_141808/vis_data/scalars.json new file mode 100644 index 0000000000..b73514dfc9 --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240920_141808/vis_data/scalars.json @@ -0,0 +1,211 @@ +{"base_lr": 1.6011257771847904e-06, "lr": 1.6011257771847904e-06, "data_time": 0.003428483009338379, "loss": 3.0422013998031616, "decode.loss_ce": 3.0422013998031616, "decode.acc_seg": 8.04440975189209, "time": 0.10466759204864502, "iter": 41, "memory": 1871, "step": 41} +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.0036227941513061524, 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"step": 9950} +{"base_lr": 0.0, "lr": 0.0, "data_time": 0.0034424543380737306, "loss": 0.021517593041062356, "decode.loss_ce": 0.021517593041062356, "decode.acc_seg": 98.90995788574219, "time": 0.10228691101074219, "iter": 10000, "memory": 1092, "step": 10000} +{"aAcc": 98.53, "mIoU": 85.25, "mAcc": 91.51, "data_time": 0.0023306846618652345, "time": 0.020103764533996583, "step": 10000} diff --git a/my_projects/best_models/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512.py b/my_projects/best_models/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512.py new file mode 100644 index 0000000000..98319fec51 --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512.py @@ -0,0 +1,321 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_143546/vis_data/20240920_143546.json 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88.42, "mAcc": 93.12, "data_time": 0.0023869752883911135, "time": 0.022557401657104494, "step": 10000} diff --git a/my_projects/best_models/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_143546/vis_data/config.py b/my_projects/best_models/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_143546/vis_data/config.py new file mode 100644 index 0000000000..be08b5a6df --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_143546/vis_data/config.py @@ -0,0 +1,343 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=26, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_143546/vis_data/scalars.json b/my_projects/best_models/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_143546/vis_data/scalars.json new file mode 100644 index 0000000000..bd039afb70 --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_143546/vis_data/scalars.json @@ -0,0 +1,211 @@ +{"base_lr": 1.6011257771847904e-06, "lr": 1.6011257771847904e-06, "data_time": 0.00507822036743164, "loss": 2.775026488304138, "decode.loss_ce": 2.775026488304138, "decode.acc_seg": 8.453996658325195, "time": 0.1579904317855835, "iter": 41, "memory": 3228, "step": 41} +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.004999637603759766, "loss": 2.919499945640564, "decode.loss_ce": 2.919499945640564, "decode.acc_seg": 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1.2001600213361763e-06, "lr": 1.2001600213361763e-06, "data_time": 0.0047750711441040036, "loss": 0.02297932580113411, "decode.loss_ce": 0.02297932580113411, "decode.acc_seg": 98.76346588134766, "time": 0.14946911334991456, "iter": 9850, "memory": 1308, "step": 9850} +{"base_lr": 8.001066808907855e-07, "lr": 8.001066808907855e-07, "data_time": 0.00482645034790039, "loss": 0.018679415434598924, "decode.loss_ce": 0.018679415434598924, "decode.acc_seg": 98.59180450439453, "time": 0.15215177536010743, "iter": 9900, "memory": 1308, "step": 9900} +{"base_lr": 4.000533404453931e-07, "lr": 4.000533404453931e-07, "data_time": 0.0047910213470458984, "loss": 0.020692188385874034, "decode.loss_ce": 0.020692188385874034, "decode.acc_seg": 98.72303009033203, "time": 0.15138788223266603, "iter": 9950, "memory": 1308, "step": 9950} +{"base_lr": 0.0, "lr": 0.0, "data_time": 0.004697990417480469, "loss": 0.023846511729061604, "decode.loss_ce": 0.023846511729061604, "decode.acc_seg": 99.15471649169922, "time": 0.144608473777771, "iter": 10000, "memory": 1308, "step": 10000} +{"aAcc": 98.9, "mIoU": 88.42, "mAcc": 93.12, "data_time": 0.0023869752883911135, "time": 0.022557401657104494, "step": 10000} diff --git a/my_projects/best_models/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py b/my_projects/best_models/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py new file mode 100644 index 0000000000..be08b5a6df --- /dev/null +++ b/my_projects/best_models/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py @@ -0,0 +1,343 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=26, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/20240805_024317/vis_data/scalars.json b/my_projects/best_models/selection_trained/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/20240805_024317/vis_data/scalars.json new file mode 100644 index 0000000000..64d9fb8339 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/20240805_024317/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 1.97} +{"step": 2000, "mIoU": 19.79} +{"step": 3000, "mIoU": 40.13} +{"step": 4000, "mIoU": 51.99} +{"step": 5000, "mIoU": 63.36} +{"step": 6000, "mIoU": 68.4} +{"step": 7000, "mIoU": 68.55} +{"step": 8000, "mIoU": 70.28} +{"step": 9000, "mIoU": 72.84} +{"step": 10000, "mIoU": 72.8} diff --git a/my_projects/best_models/selection_trained/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512.py b/my_projects/best_models/selection_trained/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..8b4fa27721 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512.py @@ -0,0 +1,336 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_042040/vis_data/scalars.json b/my_projects/best_models/selection_trained/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_042040/vis_data/scalars.json new file mode 100644 index 0000000000..9d5cbec6e2 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_042040/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 47.08} +{"step": 2000, "mIoU": 73.71} +{"step": 3000, "mIoU": 86.34} +{"step": 4000, "mIoU": 85.83} +{"step": 5000, "mIoU": 83.01} +{"step": 6000, "mIoU": 84.03} +{"step": 7000, "mIoU": 85.76} +{"step": 8000, "mIoU": 88.94} +{"step": 9000, "mIoU": 90.91} +{"step": 10000, "mIoU": 88.91} diff --git a/my_projects/best_models/selection_trained/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py b/my_projects/best_models/selection_trained/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..103405e14f --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py @@ -0,0 +1,475 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_061403/vis_data/scalars.json b/my_projects/best_models/selection_trained/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_061403/vis_data/scalars.json new file mode 100644 index 0000000000..3f3699e26d --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_061403/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 1.66} +{"step": 2000, "mIoU": 14.13} +{"step": 3000, "mIoU": 25.94} +{"step": 4000, "mIoU": 47.14} +{"step": 5000, "mIoU": 62.73} +{"step": 6000, "mIoU": 73.38} +{"step": 7000, "mIoU": 80.31} +{"step": 8000, "mIoU": 82.67} +{"step": 9000, "mIoU": 83.12} +{"step": 10000, "mIoU": 83.92} diff --git a/my_projects/best_models/selection_trained/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py b/my_projects/best_models/selection_trained/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..f38140688b --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py @@ -0,0 +1,565 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/20240805_071527/vis_data/scalars.json b/my_projects/best_models/selection_trained/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/20240805_071527/vis_data/scalars.json new file mode 100644 index 0000000000..ca7274c96e --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/20240805_071527/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 4.11} +{"step": 2000, "mIoU": 55.63} +{"step": 3000, "mIoU": 67.65} +{"step": 4000, "mIoU": 78.75} +{"step": 5000, "mIoU": 82.38} +{"step": 6000, "mIoU": 82.57} +{"step": 7000, "mIoU": 84.88} +{"step": 8000, "mIoU": 83.25} +{"step": 9000, "mIoU": 84.23} +{"step": 10000, "mIoU": 85.13} diff --git a/my_projects/best_models/selection_trained/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512.py b/my_projects/best_models/selection_trained/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..7c56d446e5 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512.py @@ -0,0 +1,322 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_080116/vis_data/scalars.json b/my_projects/best_models/selection_trained/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_080116/vis_data/scalars.json new file mode 100644 index 0000000000..c676752b87 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_080116/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 5.71} +{"step": 2000, "mIoU": 50.8} +{"step": 3000, "mIoU": 70.42} +{"step": 4000, "mIoU": 80.92} +{"step": 5000, "mIoU": 82.79} +{"step": 6000, "mIoU": 85.41} +{"step": 7000, "mIoU": 85.65} +{"step": 8000, "mIoU": 87.74} +{"step": 9000, "mIoU": 87.87} +{"step": 10000, "mIoU": 88.97} diff --git a/my_projects/best_models/selection_trained/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py b/my_projects/best_models/selection_trained/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..5d8bf45d9c --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py @@ -0,0 +1,344 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=72, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/20240924_110746.json b/my_projects/best_models/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/20240924_110746.json new file mode 100644 index 0000000000..1ed4f0a39f --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/20240924_110746.json @@ -0,0 +1,210 @@ +{"lr": 0.0006470980653769185, "data_time": 0.002445840835571289, "loss": 1.4245377123355865, "decode.loss_ce": 0.460920050740242, "decode.acc_seg": 96.78573608398438, "aux_0.loss_ce": 0.43786360919475553, "aux_0.acc_seg": 96.78573608398438, "aux_1.loss_ce": 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index 0000000000..e3436abb10 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/config.py @@ -0,0 +1,336 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/scalars.json b/my_projects/best_models/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/scalars.json new file mode 100644 index 0000000000..1ed4f0a39f --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"lr": 0.0006470980653769185, "data_time": 0.002445840835571289, "loss": 1.4245377123355865, "decode.loss_ce": 0.460920050740242, "decode.acc_seg": 96.78573608398438, "aux_0.loss_ce": 0.43786360919475553, "aux_0.acc_seg": 96.78573608398438, "aux_1.loss_ce": 0.5257540553808212, "aux_1.acc_seg": 96.78573608398438, "time": 0.09498627185821533, "iter": 50, "memory": 2550, 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0000000000..e3436abb10 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,336 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_112623/vis_data/20240924_112623.json b/my_projects/best_models/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_112623/vis_data/20240924_112623.json new file mode 100644 index 0000000000..97a38b5081 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_112623/vis_data/20240924_112623.json @@ -0,0 +1,210 @@ +{"base_lr": 9.955884763429715e-05, "lr": 9.955884763429715e-06, "data_time": 0.00566253662109375, "grad_norm": 92.14458770751953, "loss": 12.631502532958985, "decode.loss_cls": 0.34186268597841263, "decode.loss_mask": 0.0973613753914833, "decode.loss_dice": 0.1479683645069599, "decode.d0.loss_cls": 6.51755952835083, "decode.d0.loss_mask": 0.14761312752962114, "decode.d0.loss_dice": 0.27674773782491685, 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a/my_projects/best_models/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_112623/vis_data/config.py b/my_projects/best_models/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_112623/vis_data/config.py new file mode 100644 index 0000000000..d802256f03 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_112623/vis_data/config.py @@ -0,0 +1,433 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_112623/vis_data/scalars.json 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b/my_projects/best_models/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..d802256f03 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,433 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_124010/vis_data/20240924_124010.json 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0.0016722360589483288, "decode.d4.loss_mask": 0.00552055579610169, "decode.d4.loss_dice": 0.013965561613440513, "time": 0.2539757966995239, "iter": 10000, "memory": 2429, "step": 10000} +{"aAcc": 99.71, "mIoU": 89.28, "mAcc": 94.38, "data_time": 0.0016840457916259765, "time": 0.047839935620625815, "step": 10000} diff --git a/my_projects/best_models/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_124010/vis_data/config.py b/my_projects/best_models/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_124010/vis_data/config.py new file mode 100644 index 0000000000..28082765fa --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_124010/vis_data/config.py @@ -0,0 +1,523 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_124010/vis_data/scalars.json b/my_projects/best_models/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_124010/vis_data/scalars.json new file mode 100644 index 0000000000..00e6bb52ec --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_124010/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 0.004952335357666015, "grad_norm": 589.998159790039, "loss": 23.99028034210205, "decode.loss_cls": 3.7930330514907835, "decode.loss_mask": 0.11582119278609752, "decode.loss_dice": 0.20272027775645257, "decode.d0.loss_cls": 3.5208041667938232, "decode.d0.loss_mask": 0.0862992450594902, "decode.d0.loss_dice": 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mode 100644 index 0000000000..28082765fa --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,523 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/20240924_132330.json b/my_projects/best_models/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/20240924_132330.json new file mode 100644 index 0000000000..f601f3a3c6 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/20240924_132330.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.003247380256652832, "loss": 3.061506676673889, "decode.loss_ce": 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"iter": 9950, "memory": 1092, "step": 9950} +{"base_lr": 0.0, "lr": 0.0, "data_time": 0.003294682502746582, "loss": 0.00769683497492224, "decode.loss_ce": 0.00769683497492224, "decode.acc_seg": 99.67753601074219, "time": 0.09499402046203613, "iter": 10000, "memory": 1092, "step": 10000} +{"aAcc": 99.62, "mIoU": 87.51, "mAcc": 94.92, "data_time": 0.0015500783920288086, "time": 0.01838030815124512, "step": 10000} diff --git a/my_projects/best_models/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/config.py b/my_projects/best_models/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/config.py new file mode 100644 index 0000000000..9268b6dc88 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/config.py @@ -0,0 +1,322 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/scalars.json 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b/my_projects/best_models/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..9268b6dc88 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,322 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_133957/vis_data/20240924_133957.json b/my_projects/best_models/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_133957/vis_data/20240924_133957.json new file mode 100644 index 0000000000..7dea46ef02 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_133957/vis_data/20240924_133957.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.004604220390319824, "loss": 2.9579598188400267, 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'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=30, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_133957/vis_data/scalars.json b/my_projects/best_models/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_133957/vis_data/scalars.json new file mode 100644 index 0000000000..7dea46ef02 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_133957/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.004604220390319824, "loss": 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+1,344 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=30, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20241015_093911/vis_data/20241015_093911.json b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20241015_093911/vis_data/20241015_093911.json new file mode 100644 index 0000000000..32cde5bebf --- /dev/null +++ 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0.00163880189259847, "time": 0.013609766960144043, "step": 10000} diff --git a/my_projects/best_models/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20241015_093911/vis_data/config.py b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20241015_093911/vis_data/config.py new file mode 100644 index 0000000000..82d69eb6c6 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20241015_093911/vis_data/config.py @@ -0,0 +1,337 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20241015_093911/vis_data/scalars.json b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20241015_093911/vis_data/scalars.json new file mode 100644 index 0000000000..32cde5bebf --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20241015_093911/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"lr": 0.0006470980653769185, "data_time": 0.002411675453186035, 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a/my_projects/best_models/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512.py b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..82d69eb6c6 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,337 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_095615/vis_data/20241015_095615.json b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_095615/vis_data/20241015_095615.json new file mode 100644 index 0000000000..8172b0db92 --- /dev/null +++ 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0.09262319132685662, "time": 0.4003201961517334, "iter": 10000, "memory": 2865, "step": 10000} +{"aAcc": 99.73, "mIoU": 86.16, "mAcc": 93.31, "data_time": 0.0016568740208943685, "time": 0.07015175819396972, "step": 10000} diff --git a/my_projects/best_models/selection_trained/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_095615/vis_data/config.py b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_095615/vis_data/config.py new file mode 100644 index 0000000000..2d50ecbeec --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_095615/vis_data/config.py @@ -0,0 +1,434 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 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a/my_projects/best_models/selection_trained/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..2d50ecbeec --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,434 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_110407/vis_data/20241015_110407.json b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_110407/vis_data/20241015_110407.json new file mode 100644 index 0000000000..2dbc22fdf3 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_110407/vis_data/20241015_110407.json @@ -0,0 +1,210 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 0.004987859725952148, "grad_norm": 621.3393798828125, "loss": 22.60122127532959, "decode.loss_cls": 3.2704917192459106, "decode.loss_mask": 0.14756483882665633, "decode.loss_dice": 0.17138329669833183, "decode.d0.loss_cls": 3.5796732902526855, "decode.d0.loss_mask": 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"decode.d4.loss_mask": 0.010207669204100966, "decode.d4.loss_dice": 0.019936266541481017, "time": 0.25200376510620115, "iter": 9950, "memory": 2429, "step": 9950} +{"base_lr": 0.0, "lr": 0.0, "data_time": 0.005083942413330078, "grad_norm": 4.320200711488724, "loss": 0.27931518107652664, "decode.loss_cls": 0.0077530091370135775, "decode.loss_mask": 0.007839737832546234, "decode.loss_dice": 0.029057373758405447, "decode.d0.loss_cls": 0.0126910609746119, "decode.d0.loss_mask": 0.009428284596651793, "decode.d0.loss_dice": 0.030798789393156768, "decode.d1.loss_cls": 0.007532276440178976, "decode.d1.loss_mask": 0.008355768164619803, "decode.d1.loss_dice": 0.03059236314147711, "decode.d2.loss_cls": 0.008669024708797223, "decode.d2.loss_mask": 0.008117251843214036, "decode.d2.loss_dice": 0.029108216520398855, "decode.d3.loss_cls": 0.00819929042700096, "decode.d3.loss_mask": 0.007855188194662332, "decode.d3.loss_dice": 0.028710821457207204, "decode.d4.loss_cls": 0.007639522684621624, "decode.d4.loss_mask": 0.007871839171275496, "decode.d4.loss_dice": 0.029095346760004758, "time": 0.25204076766967776, "iter": 10000, "memory": 2466, "step": 10000} +{"aAcc": 99.69, "mIoU": 89.23, "mAcc": 94.35, "data_time": 0.0016114155451456705, "time": 0.047709202766418456, "step": 10000} diff --git a/my_projects/best_models/selection_trained/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_110407/vis_data/config.py b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_110407/vis_data/config.py new file mode 100644 index 0000000000..6c7cf167da --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_110407/vis_data/config.py @@ -0,0 +1,524 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_110407/vis_data/scalars.json b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_110407/vis_data/scalars.json new file mode 100644 index 0000000000..2dbc22fdf3 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_110407/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 0.004987859725952148, "grad_norm": 621.3393798828125, "loss": 22.60122127532959, "decode.loss_cls": 3.2704917192459106, "decode.loss_mask": 0.14756483882665633, "decode.loss_dice": 0.17138329669833183, "decode.d0.loss_cls": 3.5796732902526855, "decode.d0.loss_mask": 0.1255863331258297, "decode.d0.loss_dice": 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b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,524 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20241015_114713/vis_data/20241015_114713.json b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20241015_114713/vis_data/20241015_114713.json new file mode 100644 index 0000000000..332657c969 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20241015_114713/vis_data/20241015_114713.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.003410649299621582, "loss": 3.111463022232056, "decode.loss_ce": 3.111463022232056, "decode.acc_seg": 20.90740203857422, "time": 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0.0, "data_time": 0.0033080339431762694, "loss": 0.01139851645566523, "decode.loss_ce": 0.01139851645566523, "decode.acc_seg": 97.73998260498047, "time": 0.09523732662200927, "iter": 10000, "memory": 1026, "step": 10000} +{"aAcc": 99.6, "mIoU": 86.9, "mAcc": 94.38, "data_time": 0.0015838146209716797, "time": 0.018149908383687338, "step": 10000} diff --git a/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20241015_114713/vis_data/config.py b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20241015_114713/vis_data/config.py new file mode 100644 index 0000000000..2b5872074c --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20241015_114713/vis_data/config.py @@ -0,0 +1,323 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20241015_114713/vis_data/scalars.json b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20241015_114713/vis_data/scalars.json new file mode 100644 index 0000000000..332657c969 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20241015_114713/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.003410649299621582, "loss": 3.111463022232056, "decode.loss_ce": 3.111463022232056, "decode.acc_seg": 20.90740203857422, "time": 0.09794931411743164, "iter": 50, "memory": 2002, "step": 50} +{"base_lr": 3.962697798532355e-06, "lr": 3.962697798532355e-06, "data_time": 0.0032634973526000977, "loss": 2.607606840133667, "decode.loss_ce": 2.607606840133667, 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10000} +{"aAcc": 99.6, "mIoU": 86.9, "mAcc": 94.38, "data_time": 0.0015838146209716797, "time": 0.018149908383687338, "step": 10000} diff --git a/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..2b5872074c --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,323 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/20241015_120332/vis_data/20241015_120332.json b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/20241015_120332/vis_data/20241015_120332.json new file mode 100644 index 0000000000..edbb9e7053 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/20241015_120332/vis_data/20241015_120332.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.004528188705444336, "loss": 2.8272652983665467, "decode.loss_ce": 2.8272652983665467, "decode.acc_seg": 11.895751953125, "time": 0.14495186805725097, "iter": 50, "memory": 3231, "step": 50} +{"base_lr": 3.962697798532355e-06, "lr": 3.962697798532355e-06, "data_time": 0.004592514038085938, "loss": 2.7296712398529053, "decode.loss_ce": 2.7296712398529053, "decode.acc_seg": 89.89390563964844, "time": 0.14464640617370605, "iter": 100, "memory": 1333, "step": 100} +{"base_lr": 5.964030020013346e-06, "lr": 5.964030020013346e-06, "data_time": 0.004427433013916016, "loss": 1.9587167978286744, "decode.loss_ce": 1.9587167978286744, "decode.acc_seg": 84.3954086303711, 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"iter": 9900, "memory": 1333, "step": 9900} +{"base_lr": 3.529827038475068e-07, "lr": 3.529827038475068e-07, "data_time": 0.004631495475769043, "loss": 0.011922908062115312, "decode.loss_ce": 0.011922908062115312, "decode.acc_seg": 99.71866607666016, "time": 0.14250097274780274, "iter": 9950, "memory": 1332, "step": 9950} +{"base_lr": 0.0, "lr": 0.0, "data_time": 0.004599809646606445, "loss": 0.009129674686118961, "decode.loss_ce": 0.009129674686118961, "decode.acc_seg": 99.69921112060547, "time": 0.1427034854888916, "iter": 10000, "memory": 1332, "step": 10000} +{"aAcc": 99.7, "mIoU": 90.06, "mAcc": 94.76, "data_time": 0.0017589886983235678, "time": 0.02174348831176758, "step": 10000} diff --git a/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/20241015_120332/vis_data/config.py b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/20241015_120332/vis_data/config.py new file mode 100644 index 0000000000..ac75c7a729 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/20241015_120332/vis_data/config.py @@ -0,0 +1,345 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=30, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512' diff --git a/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/20241015_120332/vis_data/scalars.json b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/20241015_120332/vis_data/scalars.json new file mode 100644 index 0000000000..edbb9e7053 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/20241015_120332/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.004528188705444336, "loss": 2.8272652983665467, "decode.loss_ce": 2.8272652983665467, "decode.acc_seg": 11.895751953125, "time": 0.14495186805725097, "iter": 50, "memory": 3231, "step": 50} +{"base_lr": 3.962697798532355e-06, "lr": 3.962697798532355e-06, "data_time": 0.004592514038085938, "loss": 2.7296712398529053, "decode.loss_ce": 2.7296712398529053, "decode.acc_seg": 89.89390563964844, "time": 0.14464640617370605, "iter": 100, "memory": 1333, "step": 100} +{"base_lr": 5.964030020013346e-06, "lr": 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a/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..ac75c7a729 --- /dev/null +++ b/my_projects/best_models/selection_trained/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,345 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=30, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 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--- /dev/null +++ b/my_projects/best_models/selection_trained/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/20241027_191703/vis_data/config.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512' diff --git a/my_projects/best_models/selection_trained/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/20241027_191703/vis_data/scalars.json b/my_projects/best_models/selection_trained/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/20241027_191703/vis_data/scalars.json new file mode 100644 index 0000000000..df14178012 --- /dev/null +++ b/my_projects/best_models/selection_trained/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/20241027_191703/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"lr": 0.0006470980653769185, "data_time": 0.0025153160095214844, "loss": 1.3102005422115326, "decode.loss_ce": 0.42309882640838625, "decode.acc_seg": 93.8547134399414, "aux_0.loss_ce": 0.450121234357357, "aux_0.acc_seg": 93.8547134399414, "aux_1.loss_ce": 0.43698047399520873, "aux_1.acc_seg": 93.8547134399414, "time": 0.0948939323425293, "iter": 50, "memory": 2548, "step": 50} +{"lr": 0.0007971981320880594, "data_time": 0.002527189254760742, "loss": 1.5039239048957824, "decode.loss_ce": 0.48357090950012205, "decode.acc_seg": 92.5853500366211, "aux_0.loss_ce": 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bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512' diff --git a/my_projects/best_models/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_193429/vis_data/20241027_193429.json b/my_projects/best_models/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_193429/vis_data/20241027_193429.json new file mode 100644 index 0000000000..43eaf33010 --- /dev/null +++ b/my_projects/best_models/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_193429/vis_data/20241027_193429.json @@ -0,0 +1,210 @@ 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0.013443533501413186, "decode.d8.loss_mask": 0.09279430918395519, "decode.d8.loss_dice": 0.16148251444101333, "time": 0.4018991470336914, "iter": 10000, "memory": 2911, "step": 10000} +{"aAcc": 99.56, "mIoU": 91.36, "mAcc": 95.14, "data_time": 0.0016731054354936648, "time": 0.07098609973222782, "step": 10000} diff --git a/my_projects/best_models/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_193429/vis_data/config.py b/my_projects/best_models/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_193429/vis_data/config.py new file mode 100644 index 0000000000..978405a37e --- /dev/null +++ b/my_projects/best_models/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_193429/vis_data/config.py @@ -0,0 +1,431 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=31, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 31 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512' diff --git a/my_projects/best_models/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_193429/vis_data/scalars.json b/my_projects/best_models/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_193429/vis_data/scalars.json new file mode 100644 index 0000000000..43eaf33010 --- /dev/null +++ b/my_projects/best_models/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_193429/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 9.955884763429715e-05, "lr": 9.955884763429715e-06, "data_time": 0.0051276922225952145, "grad_norm": 177.94206733703612, "loss": 18.886375427246094, "decode.loss_cls": 0.48950027823448183, "decode.loss_mask": 0.22267264798283576, "decode.loss_dice": 0.522909551858902, "decode.d0.loss_cls": 6.529542350769043, "decode.d0.loss_mask": 0.2583734855055809, "decode.d0.loss_dice": 0.6232244476675988, "decode.d1.loss_cls": 0.5642811685800553, "decode.d1.loss_mask": 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b/my_projects/best_models/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512.py new file mode 100644 index 0000000000..978405a37e --- /dev/null +++ b/my_projects/best_models/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512.py @@ -0,0 +1,431 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=31, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 31 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512' diff --git a/my_projects/best_models/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_204248/vis_data/20241027_204248.json b/my_projects/best_models/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_204248/vis_data/20241027_204248.json new file mode 100644 index 0000000000..30bb917303 --- /dev/null +++ b/my_projects/best_models/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_204248/vis_data/20241027_204248.json @@ -0,0 +1,210 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 0.004942417144775391, "grad_norm": 551.2614044189453, "loss": 24.15789852142334, "decode.loss_cls": 3.701165723800659, "decode.loss_mask": 0.1461197104305029, "decode.loss_dice": 0.21163225248456002, "decode.d0.loss_cls": 3.7388782501220703, "decode.d0.loss_mask": 0.10264454819262028, "decode.d0.loss_dice": 0.2040844090282917, "decode.d1.loss_cls": 3.677092456817627, "decode.d1.loss_mask": 0.10647898074239492, "decode.d1.loss_dice": 0.202810388058424, "decode.d2.loss_cls": 3.5828951835632323, "decode.d2.loss_mask": 0.1574656806886196, "decode.d2.loss_dice": 0.20683653354644777, "decode.d3.loss_cls": 3.6288596630096435, "decode.d3.loss_mask": 0.17856678888201713, "decode.d3.loss_dice": 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"decode.d4.loss_dice": 0.05100994491949677, "time": 0.2572995662689209, "iter": 10000, "memory": 2658, "step": 10000} +{"aAcc": 99.5, "mIoU": 86.46, "mAcc": 90.96, "data_time": 0.001585232905852489, "time": 0.047689370619945035, "step": 10000} diff --git a/my_projects/best_models/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_204248/vis_data/config.py b/my_projects/best_models/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_204248/vis_data/config.py new file mode 100644 index 0000000000..534c342793 --- /dev/null +++ b/my_projects/best_models/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_204248/vis_data/config.py @@ -0,0 +1,521 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=31, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 31 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512' diff --git a/my_projects/best_models/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_204248/vis_data/scalars.json b/my_projects/best_models/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_204248/vis_data/scalars.json new file mode 100644 index 0000000000..30bb917303 --- /dev/null +++ b/my_projects/best_models/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_204248/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 0.004942417144775391, "grad_norm": 551.2614044189453, "loss": 24.15789852142334, "decode.loss_cls": 3.701165723800659, "decode.loss_mask": 0.1461197104305029, "decode.loss_dice": 0.21163225248456002, "decode.d0.loss_cls": 3.7388782501220703, "decode.d0.loss_mask": 0.10264454819262028, "decode.d0.loss_dice": 0.2040844090282917, "decode.d1.loss_cls": 3.677092456817627, "decode.d1.loss_mask": 0.10647898074239492, "decode.d1.loss_dice": 0.202810388058424, "decode.d2.loss_cls": 3.5828951835632323, "decode.d2.loss_mask": 0.1574656806886196, "decode.d2.loss_dice": 0.20683653354644777, "decode.d3.loss_cls": 3.6288596630096435, "decode.d3.loss_mask": 0.17856678888201713, "decode.d3.loss_dice": 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0.034812304005026815, "time": 0.25473501682281496, "iter": 9900, "memory": 2625, "step": 9900} +{"base_lr": 6.001200240048298e-07, "lr": 6.001200240048298e-07, "data_time": 0.005143165588378906, "grad_norm": 16.67101159095764, "loss": 0.47842560410499574, "decode.loss_cls": 0.01112652532174252, "decode.loss_mask": 0.017157373623922466, "decode.loss_dice": 0.04569944217801094, "decode.d0.loss_cls": 0.032974167400971054, "decode.d0.loss_mask": 0.01804108954966068, "decode.d0.loss_dice": 0.048898009955883025, "decode.d1.loss_cls": 0.015641956168110482, "decode.d1.loss_mask": 0.01732458658516407, "decode.d1.loss_dice": 0.04580374043434858, "decode.d2.loss_cls": 0.012423941072484013, "decode.d2.loss_mask": 0.01760973990894854, "decode.d2.loss_dice": 0.045644632913172244, "decode.d3.loss_cls": 0.011596026025654283, "decode.d3.loss_mask": 0.017472879961133003, "decode.d3.loss_dice": 0.04565973691642285, "decode.d4.loss_cls": 0.01117402053787373, "decode.d4.loss_mask": 0.017490574950352313, "decode.d4.loss_dice": 0.046687173470854756, "time": 0.2548895120620728, "iter": 9950, "memory": 2584, "step": 9950} +{"base_lr": 0.0, "lr": 0.0, "data_time": 0.005183577537536621, "grad_norm": 14.384240597486496, "loss": 0.5316968865692615, "decode.loss_cls": 0.017620243260171264, "decode.loss_mask": 0.013965326268225909, "decode.loss_dice": 0.05104165645316243, "decode.d0.loss_cls": 0.03498342430102639, "decode.d0.loss_mask": 0.014202537620440126, "decode.d0.loss_dice": 0.05364836826920509, "decode.d1.loss_cls": 0.0282955261063762, "decode.d1.loss_mask": 0.013731927890330554, "decode.d1.loss_dice": 0.0501942740753293, "decode.d2.loss_cls": 0.02403781462344341, "decode.d2.loss_mask": 0.013845808990299701, "decode.d2.loss_dice": 0.05141396401450038, "decode.d3.loss_cls": 0.017742824487504548, "decode.d3.loss_mask": 0.013708711322396993, "decode.d3.loss_dice": 0.05104266097769141, "decode.d4.loss_cls": 0.017417740113160107, "decode.d4.loss_mask": 0.013794129807502031, "decode.d4.loss_dice": 0.05100994491949677, "time": 0.2572995662689209, "iter": 10000, "memory": 2658, "step": 10000} +{"aAcc": 99.5, "mIoU": 86.46, "mAcc": 90.96, "data_time": 0.001585232905852489, "time": 0.047689370619945035, "step": 10000} diff --git a/my_projects/best_models/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512.py b/my_projects/best_models/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512.py new file mode 100644 index 0000000000..534c342793 --- /dev/null +++ b/my_projects/best_models/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512.py @@ -0,0 +1,521 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=31, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 31 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512' diff --git a/my_projects/best_models/selection_trained/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/20241027_212628/vis_data/20241027_212628.json 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a/my_projects/best_models/selection_trained/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/20241027_212628/vis_data/config.py b/my_projects/best_models/selection_trained/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/20241027_212628/vis_data/config.py new file mode 100644 index 0000000000..58f4676b4c --- /dev/null +++ b/my_projects/best_models/selection_trained/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/20241027_212628/vis_data/config.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_SODHOTS-C-512x512' diff --git a/my_projects/best_models/selection_trained/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/20241027_212628/vis_data/scalars.json b/my_projects/best_models/selection_trained/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/20241027_212628/vis_data/scalars.json new file mode 100644 index 0000000000..8af950fb60 --- /dev/null +++ b/my_projects/best_models/selection_trained/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/20241027_212628/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.0033128023147583007, "loss": 2.994530510902405, "decode.loss_ce": 2.994530510902405, "decode.acc_seg": 25.76491928100586, "time": 0.09923000335693359, "iter": 50, "memory": 2032, "step": 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10000, "memory": 1032, "step": 10000} +{"aAcc": 99.2, "mIoU": 84.98, "mAcc": 92.03, "data_time": 0.001484088408641326, "time": 0.018106546157445662, "step": 10000} diff --git a/my_projects/best_models/selection_trained/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/segformer_mit-b0_1xb2-10k_sodhots-c-512x512.py b/my_projects/best_models/selection_trained/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/segformer_mit-b0_1xb2-10k_sodhots-c-512x512.py new file mode 100644 index 0000000000..ae2fafcc93 --- /dev/null +++ b/my_projects/best_models/selection_trained/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/segformer_mit-b0_1xb2-10k_sodhots-c-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_sodhots-c-512x512' diff --git a/my_projects/best_models/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_214304/vis_data/20241027_214304.json b/my_projects/best_models/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_214304/vis_data/20241027_214304.json new file mode 100644 index 0000000000..9927998624 --- /dev/null +++ b/my_projects/best_models/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_214304/vis_data/20241027_214304.json @@ -0,0 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0.004836726188659668, "loss": 0.020111867878586053, "decode.loss_ce": 0.020111867878586053, "decode.acc_seg": 99.25785064697266, "time": 0.14681808948516845, "iter": 10000, "memory": 1338, "step": 10000} +{"aAcc": 99.43, "mIoU": 87.38, "mAcc": 92.79, "data_time": 0.001533991251236353, "time": 0.0221102115435478, "step": 10000} diff --git a/my_projects/best_models/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_214304/vis_data/config.py b/my_projects/best_models/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_214304/vis_data/config.py new file mode 100644 index 0000000000..9cc09b4bde --- /dev/null +++ b/my_projects/best_models/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_214304/vis_data/config.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=31, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-5k_SODHOTS-C-512x512' diff --git a/my_projects/best_models/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_214304/vis_data/scalars.json b/my_projects/best_models/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_214304/vis_data/scalars.json new file mode 100644 index 0000000000..9927998624 --- /dev/null +++ b/my_projects/best_models/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_214304/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.004518485069274903, "loss": 2.879839825630188, "decode.loss_ce": 2.879839825630188, "decode.acc_seg": 61.93370819091797, "time": 0.14556424617767333, "iter": 50, "memory": 3233, "step": 50} +{"base_lr": 3.962697798532355e-06, "lr": 3.962697798532355e-06, "data_time": 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87.38, "mAcc": 92.79, "data_time": 0.001533991251236353, "time": 0.0221102115435478, "step": 10000} diff --git a/my_projects/best_models/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512.py b/my_projects/best_models/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512.py new file mode 100644 index 0000000000..6491441345 --- /dev/null +++ b/my_projects/best_models/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=31, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_SODHOTS-C-512x512' diff --git a/my_projects/conversion_tests/converters/__init__.py b/my_projects/conversion_tests/converters/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/my_projects/conversion_tests/converters/conversion_dicts.py b/my_projects/conversion_tests/converters/conversion_dicts.py new file mode 100644 index 0000000000..d5f6cdee7d --- /dev/null +++ b/my_projects/conversion_tests/converters/conversion_dicts.py @@ -0,0 +1,413 @@ +from mmseg.utils import ( + hots_v1_classes, + hots_v1_palette, + irl_vision_sim_classes, + irl_vision_sim_palette, + ade_classes, + ade_palette, + hots_v1_cat_classes, + hots_v1_cat_palette, + irl_vision_sim_cat_classes, + irl_vision_sim_cat_palette, + arid20cat_classes, + arid20cat_palette, + arid10cat_classes, + arid10cat_palette +) + + +HOTS2HOTS_CAT = { + 0 : 0, + 1 : 1, + 2 : 2, + 3 : 3, + 4 : 3, + 5 : 3, + 6 : 4, + 7 : 5, + 8 : 5, + 9 : 5, + 10 : 5, + 11 : 5, + 12 : 6, + 13 : 6, + 14 : 6, + 15 : 7, + 16 : 7, + 17 : 8, + 18 : 8, + 19 : 8, + 20 : 9, + 21 : 10, + 22 : 11, + 23 : 12, + 24 : 13, + 25 : 13, + 26 : 14, + 27 : 14, + 28 : 15, + 29 : 16, + 30 : 16, + 31 : 17, + 32 : 18, + 33 : 19, + 34 : 20, + 35 : 20, + 36 : 20, + 37 : 21, + 38 : 21, + 39 : 22, + 40 : 22, + 41 : 22, + 42 : 23, + 43 : 23, + 44 : 24, + 45 : 24, + 46 : 25 +} + +IRL_VISION2IRL_VISION_CAT = { + 0 : 0, + 1 : 1, + 2 : 2, + 3 : 3, + 4 : 3, + 5 : 3, + 6 : 3, + 7 : 3, + 8 : 3, + 9 : 4, + 10 : 4, + 11 : 4, + 12 : 4, + 13 : 4, + 14 : 4, + 15 : 5, + 16 : 5, + 17 : 5, + 18 : 5, + 19 : 5, + 20 : 6, + 21 : 6, + 22 : 6, + 23 : 7, + 24 : 7, + 25 : 7, + 26 : 8, + 27 : 8, + 28 : 8, + 29 : 8, + 30 : 9, + 31 : 9, + 32 : 9, + 33 : 9, + 34 : 10, + 35 : 11, + 36 : 11, + 37 : 11, + 38 : 11, + 39 : 12, + 40 : 13, + 41 : 14, + 42 : 14, + 43 : 15, + 44 : 16, + 45 : 16, + 46 : 16, + 47 : 17, + 48 : 18, + 49 : 19, + 50 : 20, + 51 : 20, + 52 : 20, + 53 : 20, + 54 : 21, + 55 : 22, + 56 : 23, + 57 : 24, + 58 : 24, + 59 : 24, + 60 : 24, + 61 : 25, + 62 : 25, + 63 : 25, + 64 : 26, + 65 : 26, + 66 : 26, + 67 : 27, + 68 : 28, + 69 : 28, + 70 : 28, + 71 : 29 +} + +IRL_VISION_CAT2HOTS_CAT = { + 0 : 0, + 1 : 1, + 2 : 2, + 3 : 3, + 4 : 4, + 5 : 5, + 8 : 6, + 10 : 7, + 11 : 8, + 12 : 9, + 13 : 10, + 14 : 11, + 15 : 12, + 16 : 13, + 17 : 14, + 18 : 15, + 19 : 16, + 21 : 17, + 22 : 18, + 23 : 19, + 24 : 20, + 25 : 21, + 26 : 22, + 27 : 23, + 29 : 24 +} + +HOTS_CAT2IRL_VISION_CAT = { + 0 : 0, + 1 : 1, + 2 : 2, + 3 : 3, + 4 : 4, + 5 : 5, + 6 : 8, + 7 : 10, + 8 : 11, + 9 : 12, + 10 : 13, + 11 : 14, + 12 : 15, + 13 : 16, + 14 : 17, + 15 : 18, + 16 : 19, + 17 : 21, + 18 : 22, + 19 : 23, + 20 : 24, + 21 : 25, + 22 : 26, + 23 : 27, + 24 : 29 +} + +ADE20K2HOTS_CAT_CLASS_NAMES = { + "box" : ["juice_box", "milk"], + "book" : ["book"], + "computer" : [ + "laptop", + "monitor", + "mouse", + "keyboard" + ], + "food" : [ + "apple", + "banana", + "lemon", + "orange", + "peach", + "pear", + "pringles" + ], # solid food + "screen" : [ + "laptop", + "monitor" + ], + "crt screen" : [ + "laptop", + "monitor" + ], + "plate" : ["plate"], + "monitor" : [ + "laptop", + "monitor" + ], + "glass" : ["cup", "bowl"] +} + +ADE20K2HOTS_CAT = { + 41 : [8, 14], + 67 : [3], + 74 : [11, 15, 16, 9], + 120 : [1, 2, 12, 17, 18, 19, 22], + 130 : [11, 15], + 141 : [11, 15], + 142 : [21], + 143 : [11, 15], + 147 : [6, 4] +} + +HOTS_CAT2ADE20K = { + 8 : [41], + 14 : [41], + 3 : [67], + 11 : [74, 130, 141, 143], + 15 : [74, 130, 141, 143], + 16 : [74], + 9 : [74], + 1 : [120], + 2 : [120], + 12 : [120], + 17 : [120], + 18 : [120], + 19 : [120], + 22 : [120], + 21 : [142], + 6 : [147], + 4 : [147] +} + +# for sod2ade: +# cereal is box, +# flashlight = light + + + +ARID20CAT2HOTS_CAT_CLASS_NAMES = { + "_background_" : "_background_", + "apple" : "apple", + "banana" : "banana", + "bowl" : "bowl", + "soda_can" : "can", + "keyboard" : "keyboard", + "lemon" : "lemon", + "marker" : "marker", + "orange" : "orange", + "peach" : "peach", + "pear" : "pear", + "stapler" : "stapler" + +} + +ARID20CAT2IRL_VISION_CAT_CLASS_NAMES = { + "_background_" : "_background_", + "apple" : "apple", + "banana" : "banana", + "bowl" : "bowl", + "soda_can" : "can", + "cereal_box" : "cereal", + "flashlight" : "flashlight", + "keyboard" : "keyboard", + "lemon" : "lemon", + "marker" : "marker", + "coffee_mug" : "mug", + "orange" : "orange", + "peach" : "peach", + "pear" : "pear", + "sponge" : "sponge" +} +DATASET_CLASSES = { + "HOTS" : hots_v1_classes, + "HOTS_CAT" : hots_v1_cat_classes, + "IRL_VISION" : irl_vision_sim_classes, + "IRL_VISION_CAT": irl_vision_sim_cat_classes, + "ADE20K" : ade_classes, + "ARID20" : arid20cat_classes, + "ARID10" : arid10cat_classes +} + +def get_auto_convert(dataset_name): + return { + idx : idx for idx in range(len(DATASET_CLASSES[dataset_name]())) + } + +SOURCE_TARGET_MAP = { + "HOTS" : { + "HOTS_CAT" : [HOTS2HOTS_CAT], + "ADE20K" : [HOTS2HOTS_CAT, HOTS_CAT2ADE20K], + "IRL_VISION_CAT" : [HOTS2HOTS_CAT, HOTS_CAT2IRL_VISION_CAT], + "HOTS" : [get_auto_convert(dataset_name="HOTS")] + }, + "IRL_VISION" : { + "IRL_VISION_CAT" : [IRL_VISION2IRL_VISION_CAT], + "HOTS_CAT" : [ + IRL_VISION2IRL_VISION_CAT, + IRL_VISION_CAT2HOTS_CAT + ], + "IRL_VISION" : [get_auto_convert(dataset_name="IRL_VISION")] + }, + "ADE20K" : { + "HOTS_CAT" : [ADE20K2HOTS_CAT], + "ADE20K" : [get_auto_convert(dataset_name="ADE20K")] + }, + "IRL_VISION_CAT" : { + "HOTS_CAT" : [IRL_VISION_CAT2HOTS_CAT], + "IRL_VISION_CAT" :[ + get_auto_convert(dataset_name="IRL_VISION_CAT") + ] + }, + "HOTS_CAT" : { + "IRL_VISION_CAT" : [HOTS_CAT2IRL_VISION_CAT], + "HOTS_CAT" : [get_auto_convert(dataset_name="HOTS_CAT")] + }, + "ARID20" : { + "ARID20" : [get_auto_convert(dataset_name="ARID20")], + "ARID10" : [get_auto_convert(dataset_name="ARID10")] + }, + "ARID10" : { + "ARID20" : [get_auto_convert(dataset_name="ARID20")], + "ARID10" : [get_auto_convert(dataset_name="ARID10")] + } +} + +MISSING_CLASS_NAMES = { + "HOTS_CAT" : { + "IRL_VISION_CAT" : [ + "cap", + "cereal", + "flashlight", + "mug", + "sponge" + ] + }, + "IRL_VISION_CAT" : { + "HOTS_CAT" : [ + "stapler" + ] + }, + "HOTS" : { + "IRL_VISION" : [ + "cap", + "cereal", + "flashlight", + "mug", + "sponge" + ] + }, + "IRL_VISION" : { + "HOTS" : [ + "stapler" + ] + } +} + +def get_missing_class_names(source_dataset, target_dataset): + if "HOTS" in source_dataset: + source_dataset = "HOTS" + elif "IRL" in source_dataset: + source_dataset = "IRL_VISION" + + if "HOTS" in target_dataset: + target_dataset = "HOTS" + elif "IRL" in target_dataset: + target_dataset = "IRL_VISION" + if target_dataset == source_dataset: + return [] + return MISSING_CLASS_NAMES[source_dataset][target_dataset] + +DATASET_PALETTE = { + "HOTS" : hots_v1_palette, + "HOTS_CAT" : hots_v1_cat_palette, + "IRL_VISION" : irl_vision_sim_palette, + "IRL_VISION_CAT": irl_vision_sim_cat_palette, + "ADE20K" : ade_palette, + "ARID20" : arid20cat_palette, + "ARID10" : arid10cat_palette +} + diff --git a/my_projects/conversion_tests/converters/dataset_converter.py b/my_projects/conversion_tests/converters/dataset_converter.py new file mode 100644 index 0000000000..5822411ef0 --- /dev/null +++ b/my_projects/conversion_tests/converters/dataset_converter.py @@ -0,0 +1,379 @@ +import numpy as np +from .conversion_dicts import ( + SOURCE_TARGET_MAP as CONV_MAP, + DATASET_CLASSES as GET_CLASSES, + DATASET_PALETTE as GET_PALETTE +) +import torch + +# this is very hard coded: converting only works when 1 to 1 +# ensure test to target is always set2set_cat +class DatasetConverter: + def __init__( + self, + test_dataset: str = "HOTS", + output_dataset: str = "ADE20K", + target_dataset: str = "HOTS_CAT", + unknown_idx: int = 255 + ) -> None: + self.test_dataset = test_dataset + self.output_dataset = output_dataset + self.target_dataset = target_dataset + self.unknown_idx = unknown_idx + self.gt_conversions = CONV_MAP[test_dataset][target_dataset] + self.pred_conversions = CONV_MAP[output_dataset][target_dataset] + + self.gt_conv_direct = DatasetConverter.conversions_are_direct( + conversions=self.gt_conversions + ) + self.pred_conv_direct = DatasetConverter.conversions_are_direct( + conversions=self.pred_conversions + ) + + + + def convert_labels( + self, + pred_label, + gt_label + ): + + gt_new = self.convert_gt_label(label=gt_label) + pred_new = self.convert_pred_label( + pred_label=pred_label, + gt_label_converted=gt_new + ) + return pred_new, gt_new + + def get_intersect_and_union( + self, + pred_label, + gt_label + ): + + pred_new, gt_new = self.convert_labels( + pred_label=pred_label, + gt_label=gt_label + ) + assert len(gt_label) == len(pred_label), \ + print(f"invalid gt and pred len: \ + {len(gt_label)} vs {len(pred_label)} respectively") + + return self.get_areas(gt_converted=gt_new, pred_converted=pred_new) + + def get_areas(self, gt_converted, pred_converted): + if self.gt_conv_direct and self.pred_conv_direct: + return self.get_areas_direct( + gt_converted=gt_converted, + pred_converted=pred_converted + ) + # area_intersect is of shape (n_classes_target_dataset) + area_intersect = np.zeros( + len(GET_CLASSES[self.target_dataset]()) + ).astype(np.uint16) + area_gt = np.zeros_like(area_intersect) + area_pred_label = np.zeros_like(area_intersect) + + for (gt_val, pred_val) in zip(gt_converted, pred_converted): + if pred_val == self.unknown_idx and gt_val == self.unknown_idx: + continue + if gt_val != self.unknown_idx: + area_gt[gt_val] += 1 + if type(pred_val) is list: + for pred_val_idx in pred_val: + # TODO pred vals are unique but this could be extreme + if gt_val == pred_val_idx and gt_val != self.unknown_idx: + area_intersect[gt_val] += 1 + # TODO devided due to it being a list and it could be either + area_pred_label[pred_val_idx] += 1 + else: + if pred_val == gt_val: + area_intersect[gt_val] += 1 + if pred_val != self.unknown_idx: + area_pred_label[pred_val] += 1 + area_union = area_pred_label + area_gt - area_intersect + + return area_intersect, area_union, area_pred_label, area_gt + + + def get_areas_direct(self, gt_converted, pred_converted): + + area_intersect = np.zeros( + len(GET_CLASSES[self.target_dataset]()) + ).astype(np.uint16) + area_gt = np.zeros_like(area_intersect) + area_pred_label = np.zeros_like(area_intersect) + + for (gt_val, pred_val) in zip(gt_converted, pred_converted): + if pred_val == self.unknown_idx and gt_val == self.unknown_idx: + continue + if gt_val != self.unknown_idx: + area_gt[gt_val] += 1 + if pred_val == gt_val: + area_intersect[gt_val] += 1 + if pred_val != self.unknown_idx: + area_pred_label[pred_val] += 1 + + area_union = area_pred_label + area_gt - area_intersect + return area_intersect, area_union, area_pred_label, area_gt + + def convert_direct_label( + self, + label: np.ndarray, + conversions: list + ) -> np.ndarray: + new_label = np.zeros_like(label) + for idx, value in enumerate(label): + val = value + for conversion in conversions: + if type(val) is list: + print(f"dataconverter.convert_gt_label: \ + dict value should not be a list") + if val in conversion.keys(): + val = conversion[val] + else: + val = self.unknown_idx + new_label[idx] = val + return new_label + + + + # always known_dataset to known_dataset_cat (unless cat dataset) + def convert_gt_label( + self, + label: np.ndarray, + ) -> np.ndarray: + label_shape = label.shape + if type(label) is torch.Tensor: + label = np.asarray(label.cpu()) + label = label.flatten() + if self.gt_conv_direct: + return self.convert_direct_label( + label=label, + conversions=self.gt_conversions + ).reshape(label_shape) + + return self.convert_any_label( + label=label, + conversions=self.gt_conversions + ) + + def convert_pred_label( + self, + pred_label: np.ndarray, + gt_label_converted: np.ndarray = None + ): + label_shape = pred_label.shape + if type(pred_label) is torch.Tensor: + pred_label = np.asarray(pred_label.cpu()) + pred_label = pred_label.flatten() + if self.pred_conv_direct: + return self.convert_direct_label( + label=pred_label, + conversions=self.pred_conversions + ).reshape(label_shape) + if gt_label_converted is None: + return self.convert_any_label_random_choice( + label=pred_label, + conversions=self.pred_conversions + ).reshape(label_shape) + pred_label_tmp = self.convert_any_label( + label=pred_label, + conversions=self.pred_conversions + ) + return self.convert_pred_label_list( + pred_label=pred_label_tmp, + gt_label_converted=gt_label_converted + ).reshape(label_shape) + + def convert_pred_label_rand_choice( + self, + label: np.ndarray + ): + return self.convert_any_label_random_choice( + label=label, + conversions=self.pred_conversions + ) + + def convert_pred_label_list( + self, + pred_label, + gt_label_converted: np.ndarray + ): + if type(pred_label) is torch.Tensor: + pred_label = np.asarray(pred_label.cpu()) + new_pred_label = np.zeros_like(gt_label_converted) + for label_idx, (pred_val, gt_val) in enumerate( + zip(pred_label, gt_label_converted) + ): + if isinstance(pred_val, (list, np.ndarray)): + if len(pred_val) == 1: + new_pred_label[label_idx] = pred_val[0] + continue + for pred_val_item in pred_val: + if isinstance(gt_val, (list, np.ndarray)): + if len(gt_val) == 1: + new_pred_label[label_idx] = gt_val[0] + continue + if pred_val_item in gt_val: + new_pred_label[label_idx] = pred_val_item + continue + elif gt_val == pred_val_item and gt_val != self.unknown_idx: + new_pred_label[label_idx] = pred_val_item + break + elif isinstance(gt_val, (list, np.ndarray)): + if len(gt_val) == 1: + new_pred_label[label_idx] = gt_val[0] + continue + if pred_val in gt_val: + new_pred_label[label_idx] = pred_val + continue + elif pred_val == gt_val: + new_pred_label[label_idx] = pred_val + else: + new_pred_label[label_idx] = self.unknown_idx + return new_pred_label + + + + + + def convert_any_label(self, label: np.ndarray, conversions: list): + if type(label) is torch.Tensor: + label = np.asarray(label.cpu()) + new_label = list(np.zeros_like(label)) + for idx, value in enumerate(label): + # ititial val is not a list bc label is straight from pred + val = value + for conversion in conversions: + # converting + if val in conversion.keys(): + val = conversion[val] + + elif type(val) is list and len(val) == 1: + val = conversion[val[0]] + elif type(val) is list and len(val) > 1: + converted_vals = [] + for val_item in val: + if val_item in conversion.keys(): + converted_vals.append(conversion[val_item]) + val = converted_vals if converted_vals else self.unknown_idx + else: + val = self.unknown_idx + new_label[idx] = val + return new_label + + def convert_any_label_random_choice( + self, + label: np.ndarray, + conversions: list + ) -> np.ndarray: + if type(label) is torch.Tensor: + label = np.asarray(label.cpu()) + new_label = np.zeros_like(label) + # print(f"unique label: {np.unique(label)}") + for idx, value in enumerate(label): + val = value + for conv_idx, conversion in enumerate(conversions): + # print(f"conv idx: {conv_idx}") + + if type(val) is list: + val = self.get_random_converted_val( + val=val, + conversions=conversions, + conv_idx=conv_idx + ) + # print(f"val after choice: {val}") + if val in conversion.keys(): + # print(f"in keys: {val}") + val = conversion[val] + + # print(f" val converted: {val}") + else: + # print(f"unknown: {val}") + val = self.unknown_idx + if type(val) is list: + val = self.get_random_converted_val( + val=val, + conversions=conversions, + conv_idx=conv_idx + ) + # print(f"val assigned at idx: {val} at {idx}") + new_label[idx] = val + return new_label + + def get_random_converted_val(self, val, conversions, conv_idx): + converted_vals = [] + for val_ in val: + if val_ in conversions[conv_idx].keys(): + conv_ = conversions[conv_idx][val_] + if isinstance(conv_, list): + for conv_item in conv_: + converted_vals.append(conv_item) + else: + converted_vals.append(conv_) + + # check forward + if conv_idx < len(conversions) - 1: + converted_vals = [ + c_val for c_val in converted_vals + if c_val in conversions[conv_idx + 1].keys() + ] + # print(f"val is list: val, converted = {val}, {converted_vals}") + return np.random.choice(converted_vals) if converted_vals else self.unknown_idx + + @staticmethod + def datasets_are_direct_map( + test_dataset, + output_dataset, + target_dataset + ): + gt_conversions = CONV_MAP[test_dataset][target_dataset] + + pred_conversions = CONV_MAP[output_dataset][target_dataset] + + return ( + DatasetConverter.conversions_are_direct( + conversions=gt_conversions + ) + and + DatasetConverter.conversions_are_direct( + conversions=pred_conversions + ) + ) + + @staticmethod + def conversions_are_direct( + conversions + ): + for conversion in conversions: + for key, val in conversion.items(): + if isinstance(key, list) or isinstance(val, list): + return False + return True + + @staticmethod + def get_class_names(dataset_name): + return GET_CLASSES[dataset_name]() + + @staticmethod + def get_palette(dataset_name): + return GET_PALETTE[dataset_name]() + + @staticmethod + def get_dataset(dataset_name): + return DATASETS[dataset_name] + + +# def test(): +# gt_label = np.zeros(shape=30) +# for idx, val in enumerate(gt_label): +# gt_label[idx] = np.random.choice(list(ADE20K2HOTS_CAT.keys())) +# print(f"gt_label:\n{gt_label}") +# ds_convert = DatasetConverter() +# conversions = CONV_MAP["ADE20K"]["HOTS_CAT"] +# new_gt = ds_convert.convert_label(label=gt_label, conversions=conversions) +# print(f"new gt:\n{new_gt}") +# print(f'type index: {type(CONV_MAP["ADE20K"]["HOTS_CAT"][0][75])}') + +# # test() \ No newline at end of file diff --git a/my_projects/conversion_tests/dataset_template_configs/arid10_cat_test_data.py b/my_projects/conversion_tests/dataset_template_configs/arid10_cat_test_data.py new file mode 100644 index 0000000000..801836b6e9 --- /dev/null +++ b/my_projects/conversion_tests/dataset_template_configs/arid10_cat_test_data.py @@ -0,0 +1,32 @@ +dataset_type = "ARID10CATDataset" +data_root = "/media/ids/Ubuntu files/data/ARID10_CAT/" + +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetricConversion") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] \ No newline at end of file diff --git a/my_projects/conversion_tests/dataset_template_configs/arid20_cat_test_data.py b/my_projects/conversion_tests/dataset_template_configs/arid20_cat_test_data.py new file mode 100644 index 0000000000..ed53c33223 --- /dev/null +++ b/my_projects/conversion_tests/dataset_template_configs/arid20_cat_test_data.py @@ -0,0 +1,32 @@ +dataset_type = "ARID20CATDataset" +data_root = "/media/ids/Ubuntu files/data/ARID20_CAT/" + +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetricConversion") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] \ No newline at end of file diff --git a/my_projects/conversion_tests/dataset_template_configs/hots_cat_test_data.py b/my_projects/conversion_tests/dataset_template_configs/hots_cat_test_data.py new file mode 100644 index 0000000000..c0f9140442 --- /dev/null +++ b/my_projects/conversion_tests/dataset_template_configs/hots_cat_test_data.py @@ -0,0 +1,31 @@ +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetricConversion") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] diff --git a/my_projects/conversion_tests/dataset_template_configs/hots_test_data.py b/my_projects/conversion_tests/dataset_template_configs/hots_test_data.py new file mode 100644 index 0000000000..4add25dfff --- /dev/null +++ b/my_projects/conversion_tests/dataset_template_configs/hots_test_data.py @@ -0,0 +1,31 @@ +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetricConversion") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] diff --git a/my_projects/conversion_tests/dataset_template_configs/irl_cat_test_data.py b/my_projects/conversion_tests/dataset_template_configs/irl_cat_test_data.py new file mode 100644 index 0000000000..d26c7b3438 --- /dev/null +++ b/my_projects/conversion_tests/dataset_template_configs/irl_cat_test_data.py @@ -0,0 +1,32 @@ +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetricConversion") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] diff --git a/my_projects/conversion_tests/dataset_template_configs/irl_test_data.py b/my_projects/conversion_tests/dataset_template_configs/irl_test_data.py new file mode 100644 index 0000000000..4acbd70a4f --- /dev/null +++ b/my_projects/conversion_tests/dataset_template_configs/irl_test_data.py @@ -0,0 +1,32 @@ +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetricConversion") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] diff --git a/my_projects/conversion_tests/scripts/confusion_matrix_conversion.py b/my_projects/conversion_tests/scripts/confusion_matrix_conversion.py new file mode 100644 index 0000000000..02f4851f78 --- /dev/null +++ b/my_projects/conversion_tests/scripts/confusion_matrix_conversion.py @@ -0,0 +1,228 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import argparse +import os + +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.ticker import MultipleLocator +from mmengine.config import Config, DictAction +from mmengine.registry import init_default_scope +from mmengine.utils import mkdir_or_exist, progressbar +from mmengine.fileio import dump +from PIL import Image +from my_projects.conversion_tests.converters.dataset_converter import ( + DatasetConverter +) +from mmseg.registry import DATASETS + +init_default_scope('mmseg') + + +def parse_args(): + parser = argparse.ArgumentParser( + description='Generate confusion matrix from segmentation results') + parser.add_argument('config', help='test config file path') + parser.add_argument( + 'prediction_path', help='prediction path where test folder result') + parser.add_argument( + 'save_dir', help='directory where confusion matrix will be saved') + parser.add_argument( + '--test_dataset', + '-test_ds', + type=str, + default="HOTS", + choices=[ + "HOTS", + "IRL_VISION", + "HOTS_CAT", + "IRL_VISION_CAT", + "ARID20", + "ARID10", + "ADE20K" + ] + ) + parser.add_argument( + '--output_dataset', + '-out_ds', + type=str, + default="HOTS", + choices=[ + "HOTS", + "IRL_VISION", + "HOTS_CAT", + "IRL_VISION_CAT", + "ARID20", + "ARID10", + "ADE20K" + ] + ) + parser.add_argument( + '--target_dataset', + '-tar_ds', + type=str, + default="HOTS_CAT", + choices=[ + "HOTS_CAT", + "IRL_VISION_CAT", + "ARID20", + "ARID10", + "ADE20K" + ] + ) + + parser.add_argument( + '--color-theme', + default='winter', + help='theme of the matrix color map') + parser.add_argument( + '--title', + default='Normalized Confusion Matrix', + help='title of the matrix color map') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + args = parser.parse_args() + return args + + +def calculate_confusion_matrix( + dataset_converter: DatasetConverter, + test_dataset, + results +): + """Calculate the confusion matrix + + Args: + dataset_converter (DatasetConverter): _description_ + test_dataset (_type_): _description_ + results (_type_): results are in target_dataset labels + + Returns: + _type_: _description_ + """ + n = len( + dataset_converter.get_class_names( + dataset_name=dataset_converter.target_dataset + ) + ) + confusion_matrix = np.zeros(shape=[n, n]) + assert len(test_dataset) == len(results) + # TODO not sure if should be the test of target dataset + ignore_index = dataset_converter.unknown_idx + reduce_zero_label = test_dataset.reduce_zero_label + prog_bar = progressbar.ProgressBar(len(results)) + for idx, per_img_res in enumerate(results): + res_segm = per_img_res + gt_segm = test_dataset[idx]['data_samples'] \ + .gt_sem_seg.data.squeeze().numpy().astype(np.uint8) + + gt_segm = dataset_converter.convert_gt_label(label=gt_segm) + + gt_segm, res_segm = gt_segm.flatten(), res_segm.flatten() + if reduce_zero_label: + gt_segm = gt_segm - 1 + to_ignore = gt_segm == ignore_index + + gt_segm, res_segm = gt_segm[~to_ignore], res_segm[~to_ignore] + + # inds = n * gt_segm + res_segm + # print(np.unique(inds)) + # print(max(inds)) + # mat = np.bincount(inds, minlength=n**2).reshape(n, n) + # print(np.unique(mat)) + + # confusion_matrix += mat + for gt_pix, pred_pix in zip(gt_segm, res_segm): + if gt_pix == ignore_index or pred_pix == ignore_index: + continue + confusion_matrix[gt_pix][pred_pix] += 1 + prog_bar.update() + return confusion_matrix + + + + +def dump_confusion_data( + confusion_matrix, dataset_converter: DatasetConverter, + min_value = 0.0, save_dir=None, + ignore_diagonal = True +): + per_label_sums = confusion_matrix.sum(axis=1)[:, np.newaxis] + confusion_matrix = \ + confusion_matrix.astype(np.float32) / per_label_sums * 100 + confusion_matrix = np.nan_to_num(confusion_matrix) + conf_dict_list = [] + # row nr + labels = dataset_converter.get_class_names( + dataset_name=dataset_converter.target_dataset + ) + for gt_label_idx, gt_label in enumerate(labels): + + for pred_label_idx, pred_label in enumerate(labels): + if ignore_diagonal and gt_label_idx == pred_label_idx: + continue + conf_value = confusion_matrix[gt_label_idx][pred_label_idx] + if conf_value < min_value: + continue + conf_dict_list.append( + { + "gt_label" : gt_label, + "pred_label" : pred_label, + "score" : conf_value + } + ) + if save_dir is not None: + mkdir_or_exist(save_dir) + json_file = os.path.join(save_dir, "confusion.json") + dump(conf_dict_list, json_file, indent=4) + +def main(): + args = parse_args() + # cfg should be of test_dataset + # predictions are in target + # out_ds? + cfg = Config.fromfile(args.config) + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + + dataset_converter = DatasetConverter( + test_dataset=args.test_dataset, + output_dataset=args.output_dataset, + target_dataset=args.target_dataset + ) + + results = [] + for img in sorted(os.listdir(args.prediction_path)): + img = os.path.join(args.prediction_path, img) + image = Image.open(img) + image = np.copy(image) + results.append(image) + + assert isinstance(results, list) + if isinstance(results[0], np.ndarray): + pass + else: + raise TypeError('invalid type of prediction results') + + test_dataset = DATASETS.build(cfg.test_dataloader.dataset) + confusion_matrix = calculate_confusion_matrix( + dataset_converter=dataset_converter, + test_dataset=test_dataset, + results=results + ) + dump_confusion_data( + confusion_matrix=confusion_matrix, + dataset_converter=dataset_converter, + save_dir=args.save_dir + ) + + +if __name__ == '__main__': + main() diff --git a/my_projects/conversion_tests/scripts/conversion_experiment.py b/my_projects/conversion_tests/scripts/conversion_experiment.py new file mode 100644 index 0000000000..c671e7263c --- /dev/null +++ b/my_projects/conversion_tests/scripts/conversion_experiment.py @@ -0,0 +1,93 @@ +import subprocess +import os +from tools.test_selection import collect_and_organize_all_data + + +DATASET_SOURCE_PATH = { + "HOTS" : "my_projects/conversion_tests/selection_trained/hots_v1", + "IRL_VISION" : "my_projects/conversion_tests/selection_trained/irl_vision", + "HOTS_CAT" : "my_projects/conversion_tests/selection_trained/hots_v1_cat", + "IRL_VISION_CAT": "my_projects/conversion_tests/selection_trained/irl_vision_cat", + "ARID20" : "my_projects/conversion_tests/selection_trained/arid20_cat", + "ARID10" : "my_projects/conversion_tests/selection_trained/arid10_cat", + "ADE20K" : "my_projects/conversion_tests/zero_shot/models", + "SODHOTS" : "my_projects/conversion_tests/selection_trained/sodhots-c" +} + +# (test_dataset, output_dataset, target_dataset) +# with: +# - test_dataset : name of test set used, images in test loader +# - output_dataset : dataset the model is trained for and the corresponding +# labels it outputs +# - target_dataset : how the labels of both test set and output set should +# be converted: tot the target set +DATASET_SETUPS = [ + # ("HOTS_CAT", "HOTS", "HOTS_CAT"), + # ("IRL_VISION_CAT", "IRL_VISION", "IRL_VISION_CAT"), + # ("IRL_VISION_CAT", "HOTS_CAT", "IRL_VISION_CAT"), + # ("HOTS_CAT", "IRL_VISION_CAT", "HOTS_CAT") + # ("HOTS_CAT", "IRL_VISION", "HOTS_CAT"), + # ("IRL_VISION_CAT", "HOTS", "IRL_VISION_CAT") + # ("ARID20", "ARID10", "ARID20"), + ("HOTS_CAT", "ADE20K", "ADE20K") +] + +def run_conversion_test( + model_dir_path: str, + test_dataset: str, + output_dataset: str, + target_dataset: str, + test_results_path: str, + gen_cfg: bool = True, + cfg_save_dir: str = "conv_cfg" +): + subprocess.call( + [ + "python", + "my_projects/conversion_tests/scripts/conversion_test.py", + "--model_dir_path", + model_dir_path, + "--test_dataset", + test_dataset, + "--output_dataset", + output_dataset, + "--target_dataset", + target_dataset, + "--test_results_path", + test_results_path, + "--gen_cfg", + "--cfg_save_dir", + cfg_save_dir + ] + ) + + + + +def main(): + results_dir_path = "my_projects/conversion_tests/test_results" + for (test_dataset, output_dataset, target_dataset) in DATASET_SETUPS: + dataset_src_path = DATASET_SOURCE_PATH[output_dataset] + test_results_path = os.path.join( + results_dir_path, + f"{output_dataset.lower()}2{target_dataset.lower()}" + ) + for model_dir in os.listdir(dataset_src_path): + run_conversion_test( + model_dir_path=os.path.join( + dataset_src_path, + model_dir + ), + test_dataset=test_dataset, + output_dataset=output_dataset, + target_dataset=target_dataset, + test_results_path=test_results_path, + gen_cfg=True, + cfg_save_dir="conv_cfg" + ) + collect_and_organize_all_data( + results_path=test_results_path + ) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/my_projects/conversion_tests/scripts/conversion_test.py b/my_projects/conversion_tests/scripts/conversion_test.py new file mode 100644 index 0000000000..946664d40a --- /dev/null +++ b/my_projects/conversion_tests/scripts/conversion_test.py @@ -0,0 +1,286 @@ +from mmengine import Config +import argparse +import os +import subprocess +from my_projects.conversion_tests.converters.conversion_dicts import( + DATASET_CLASSES, + DATASET_PALETTE +) +from tools.test_selection import ( + run_performance_test, + run_confusion_analysis +) + + +TEST_DATASET_TEMPLATE_PATHS = { + "HOTS" : "my_projects/conversion_tests/dataset_template_configs/hots_test_data.py", + "IRL_VISION" : "my_projects/conversion_tests/dataset_template_configs/irl_test_data.py", + "HOTS_CAT" : "my_projects/conversion_tests/dataset_template_configs/hots_cat_test_data.py", + "IRL_VISION_CAT": "my_projects/conversion_tests/dataset_template_configs/irl_cat_test_data.py", + "ARID20" : "my_projects/conversion_tests/dataset_template_configs/arid20_cat_test_data.py", + "ARID10" : "my_projects/conversion_tests/dataset_template_configs/arid10_cat_test_data.py" +} + + + +def arg_parse(): + parser = argparse.ArgumentParser() + parser.add_argument( + '--model_dir_path', + '-mdp', + type=str, + default="my_projects/conversion_tests/finegrained_vs_general/selection_trained/hots_v1" + ) + parser.add_argument( + '--test_dataset', + '-test_ds', + type=str, + default="HOTS", + choices=[ + "HOTS", + "IRL_VISION", + "HOTS_CAT", + "IRL_VISION_CAT", + "ARID20", + "ARID10", + "ADE20K" + ] + ) + parser.add_argument( + '--output_dataset', + '-out_ds', + type=str, + default="HOTS", + choices=[ + "HOTS", + "IRL_VISION", + "HOTS_CAT", + "IRL_VISION_CAT", + "ARID20", + "ARID10", + "ADE20K" + ] + ) + parser.add_argument( + '--target_dataset', + '-tar_ds', + type=str, + default="HOTS_CAT", + choices=[ + "HOTS_CAT", + "IRL_VISION_CAT", + "ARID20", + "ARID10", + "ADE20K" + ] + ) + + parser.add_argument( + '--test_results_path', + '-trp', + type=str, + default="work_dirs" + ) + parser.add_argument( + '--gen_cfg', + '-g_cfg', + action='store_true' + ) + + parser.add_argument( + '--cfg_save_dir', + '-cfg_dir', + type=str, + default="conv_cfg" + ) + + args = parser.parse_args() + return args + +def conversion_test_model( + model_path: str, + cfg_path: str, + test_results_path: str, + args +): + model_name = get_model_dir_name(model_path=model_path) + + checkpoint_path = get_checkpoint_path(model_path=model_path) + work_dir_path = os.path.join( + test_results_path, + model_name + ) + + prediction_result_path = os.path.join( + test_results_path, + model_name, + "pred_results" + ) + + show_dir_path = os.path.join( + test_results_path, + model_name, + "show" + ) + run_performance_test( + cfg_path=cfg_path, + checkpoint_path=checkpoint_path, + work_dir_path=work_dir_path, + prediction_result_path=prediction_result_path, + show_dir_path=show_dir_path + ) + + confusion_matrix_save_path = os.path.join( + work_dir_path, + "confusion_matrix" + ) + run_confusion_matrix( + cfg_path=cfg_path, + prediction_result_path=prediction_result_path, + confusion_matrix_save_path=confusion_matrix_save_path, + args=args + ) + + json_path = os.path.join( + confusion_matrix_save_path, + "confusion.json" + ) + + run_confusion_analysis( + json_path=json_path, + save_dir_path=confusion_matrix_save_path, + top_n=10 + ) + + + +def run_confusion_matrix( + cfg_path, + prediction_result_path, + confusion_matrix_save_path, + args +): + subprocess.call( + [ + "python", + "my_projects/conversion_tests/scripts/confusion_matrix_conversion.py", + cfg_path, # maybe from TEST_DATASET_TEMPLATE_PATHS + prediction_result_path, + confusion_matrix_save_path, + "--test_dataset", + args.test_dataset, + "--output_dataset", + args.output_dataset, + "--target_dataset", + args.target_dataset, + ] + ) + +def get_checkpoint_path(model_path: str) -> str: + return os.path.join( + model_path, + "weights.pth" + ) + +def get_base_cfg_path(model_path: str) -> str: + return os.path.join( + model_path, + f"{get_model_dir_name(model_path=model_path)}.py" + ) + +def get_model_dir_name(model_path: str): + idx_ = -2 if model_path[-1] == '/' else -1 + return model_path.split("/")[idx_] + +def gen_conversion_cfg( + base_cfg_path: str, + args +) -> str: + cfg = Config.fromfile(base_cfg_path) + ds_template_cfg = Config.fromfile( + TEST_DATASET_TEMPLATE_PATHS[args.test_dataset] + ) + cfg.merge_from_dict(ds_template_cfg.to_dict()) + cfg.merge_from_dict( + options=dict( + test_evaluator = dict( + type="CustomIoUMetricConversion", + test_dataset=args.test_dataset, + output_dataset=args.output_dataset, + target_dataset=args.target_dataset + ) + ) + ) + + cfg.merge_from_dict( + options=dict( + visualizer = dict( + name='visualizer', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ], + test_dataset=args.test_dataset, + output_dataset=args.output_dataset, + target_dataset=args.target_dataset + ) + ) + ) + return cfg + +def dump_conversion_cfg( + cfg: Config, + save_dir_path: str, + save_file_name: str +): + if not os.path.exists(save_dir_path): + os.makedirs(save_dir_path) + cfg.dump( + os.path.join(save_dir_path, save_file_name) + ) + +def gen_conversion_cfg_name(args) -> str: + model_name = get_model_dir_name(model_path=args.model_dir_path) + model_name = model_name.split("_")[0] + test = args.test_dataset.lower() + out = args.output_dataset.lower() + target = args.target_dataset.lower() + return f"{model_name}_{test}_{out}_{target}.py" + + +def gen_conversion_cfg_dir_path(args): + return os.path.join( + args.model_dir_path, + args.cfg_save_dir + ) + + +def main(): + + args = arg_parse() + conv_cfg_dir_path = gen_conversion_cfg_dir_path(args=args) + conv_file_name = gen_conversion_cfg_name(args=args) + conv_cfg_path = os.path.join(conv_cfg_dir_path, conv_file_name) + if args.gen_cfg or not os.path.exists(conv_cfg_path): + cfg = gen_conversion_cfg( + base_cfg_path=get_base_cfg_path(args.model_dir_path), + args=args + ) + dump_conversion_cfg( + cfg=cfg, + save_dir_path=conv_cfg_dir_path, + save_file_name=conv_file_name + ) + + conversion_test_model( + model_path=args.model_dir_path, + cfg_path=conv_cfg_path, + test_results_path=args.test_results_path, + args=args + ) + + + +if __name__ == '__main__': + main() + \ No newline at end of file diff --git a/my_projects/conversion_tests/scripts/generate_cat_datasets.py b/my_projects/conversion_tests/scripts/generate_cat_datasets.py new file mode 100644 index 0000000000..6ebf3ea0bb --- /dev/null +++ b/my_projects/conversion_tests/scripts/generate_cat_datasets.py @@ -0,0 +1,97 @@ +from my_projects.conversion_tests.converters.conversion_dicts import ( + IRL_VISION2IRL_VISION_CAT, HOTS2HOTS_CAT +) +from mmseg.utils import irl_vision_sim_cat_classes, hots_v1_cat_classes +import numpy as np +from PIL import Image +import os + +DATASET_DICT = { + "HOTS_v1_cat" : HOTS2HOTS_CAT, + "irl_vision_sim_cat" : IRL_VISION2IRL_VISION_CAT +} + + +def convert_annotation_label( + label: np.ndarray, + conversion_dict: dict +) -> np.ndarray: + label_shape = label.shape + label = label.flatten() + for label_idx, label_val in enumerate(label): + label[label_idx] = conversion_dict[label_val] + return label.reshape(label_shape) + + +def save_annotation_label( + label: np.ndarray, + annotation_file_path: str +) -> None: + im = Image.fromarray(label.astype(np.uint8)) + im.save(annotation_file_path) + +def load_annotation_label(annotation_file_path: str) -> np.ndarray: + file = Image.open(annotation_file_path) + return np.array(file) + + + +def traverse_annotation_data( + annotation_dir_path: str, + conversion_dict: dict +) -> None: + unique_labels = np.asarray([0]) + for ann_phase_dir in os.listdir(annotation_dir_path): + ann_phase_dir_path = os.path.join( + annotation_dir_path, + ann_phase_dir + ) + for file_name in os.listdir(ann_phase_dir_path): + annotation_file_path = os.path.join( + ann_phase_dir_path, + file_name + ) + label = load_annotation_label( + annotation_file_path=annotation_file_path + ) + unique_labels = np.unique( + np.concatenate( + (unique_labels, np.unique(label)) + ) + ) + label = convert_annotation_label( + label=label, conversion_dict=conversion_dict + ) + save_annotation_label( + label=label, annotation_file_path=annotation_file_path + ) + print("unique labels: ") + print(unique_labels) + + +def main(): + data_path = "/media/ids/Ubuntu files/data" + annotation_path = "SemanticSegmentation/ann_dir" + for dataset_name, conversion_dict in DATASET_DICT.items(): + dataset_name = os.path.join(dataset_name) + annotation_dir_path=os.path.join( + data_path, + dataset_name, + annotation_path + ) + print(dataset_name) + traverse_annotation_data( + annotation_dir_path=annotation_dir_path, + conversion_dict=conversion_dict + ) + print(f"hots_cat: (len == {len(hots_v1_cat_classes())})") + for class_name in hots_v1_cat_classes(): + print(class_name) + + print(f"irl_cat: (len == {len(irl_vision_sim_cat_classes())})") + for class_name in irl_vision_sim_cat_classes(): + print(class_name) + +if __name__ == '__main__': + main() + \ No newline at end of file diff --git a/my_projects/conversion_tests/scripts/generate_dataconverter_dicts.py b/my_projects/conversion_tests/scripts/generate_dataconverter_dicts.py new file mode 100644 index 0000000000..18f72e7048 --- /dev/null +++ b/my_projects/conversion_tests/scripts/generate_dataconverter_dicts.py @@ -0,0 +1,201 @@ +from mmseg.utils import irl_vision_sim_classes, hots_v1_classes +from mmseg.utils import cocostuff_classes, ade_classes +from mmseg.utils import irl_vision_sim_cat_classes, hots_v1_cat_classes +from mmseg.utils import arid20cat_classes, arid20cat_palette +from my_projects.conversion_tests.converters.conversion_dicts import ( + HOTS2HOTS_CAT, IRL_VISION2IRL_VISION_CAT, + IRL_VISION_CAT2HOTS_CAT, HOTS_CAT2IRL_VISION_CAT, + ADE20K2HOTS_CAT_CLASS_NAMES, ARID20CAT2IRL_VISION_CAT_CLASS_NAMES, + ARID20CAT2HOTS_CAT_CLASS_NAMES +) + + +print(f"{'#' * 10} ARID20K vs IRL {'#' * 10}") +print("not in arid:") +for irl_name in irl_vision_sim_cat_classes(): + if irl_name not in ARID20CAT2IRL_VISION_CAT_CLASS_NAMES.values(): + print(irl_name) + +print("not in irl:") +for arid_name in arid20cat_classes(): + if arid_name not in ARID20CAT2IRL_VISION_CAT_CLASS_NAMES.keys(): + print(arid_name) + + + +print(f"{'#' * 10} ARID20K vs HOTS {'#' * 10}") +print("not in arid:") +for hots_name in hots_v1_cat_classes(): + if hots_name not in ARID20CAT2HOTS_CAT_CLASS_NAMES.values(): + print(hots_name) + +print("not in hots:") +for arid_name in arid20cat_classes(): + if arid_name not in ARID20CAT2HOTS_CAT_CLASS_NAMES.keys(): + print(arid_name) + + +print(f"{'#' * 10} HOTS vs IRL {'#' * 10}") +print("not in hots:") +for irl_name in irl_vision_sim_cat_classes(): + if irl_vision_sim_cat_classes().index(irl_name) not in HOTS_CAT2IRL_VISION_CAT.values(): + print(irl_name) + +print("not in irl:") +for hots_name in hots_v1_cat_classes(): + if hots_v1_cat_classes().index(hots_name) not in HOTS_CAT2IRL_VISION_CAT.keys(): + print(hots_name) + +# for irl_name in irl_vision_sim_cat_classes(): +# for arid_name in arid20cat_classes(): +# if ( +# irl_name in arid_name +# or +# arid_name in irl_name +# or +# irl_name == arid_name +# ): +# if arid_name not in ARID20CAT2IRL_VISION_CAT_CLASS_NAMES.keys(): +# ARID20CAT2IRL_VISION_CAT_CLASS_NAMES[arid_name] = [] +# ARID20CAT2IRL_VISION_CAT_CLASS_NAMES[arid_name].append( +# irl_name +# ) + +# for arid_name, irl_name in ARID20CAT2IRL_VISION_CAT_CLASS_NAMES.items(): +# if isinstance(irl_name, list) and len(irl_name) == 1: +# ARID20CAT2IRL_VISION_CAT_CLASS_NAMES[arid_name] = irl_name[0] + +# for arid_name, irl_name in ARID20CAT2IRL_VISION_CAT_CLASS_NAMES.items(): +# print(f' "{arid_name}" : "{irl_name}", ') + + +# items in ade20k that matter: +# "box", "table", "book", "computer", "apparel", "bottle", +# "plaything", "bag", "ball"(?), "food", "screen" +# "vase", "tray", "crt screen", "plate", "monitor", "glass" +# ADE20K2HOTS_CAT = {} +# for ade_name, hots_names in ADE20K2HOTS_CAT_CLASS_NAMES.items(): +# ade_idx=ade_classes().index(ade_name) +# if ade_idx not in ADE20K2HOTS_CAT.keys(): +# ADE20K2HOTS_CAT[ade_idx] = [] +# for hots_name in hots_names: +# ADE20K2HOTS_CAT[ade_idx].append( +# hots_v1_cat_classes().index(hots_name) +# ) + +# print("ADE20K2HOTS_CAT") +# for key, val in ADE20K2HOTS_CAT.items(): +# print(f"{key} : {val},") + +# HOTS_CAT2ADE20K = {} +# for ade_idx, hots_idxs in ADE20K2HOTS_CAT.items(): +# for hot_idx in hots_idxs: +# if hot_idx not in HOTS_CAT2ADE20K.keys(): +# HOTS_CAT2ADE20K[hot_idx] = [] +# HOTS_CAT2ADE20K[hot_idx].append(ade_idx) + +# print("\nHOTS_CAT2ADE20K") +# for key, val in HOTS_CAT2ADE20K.items(): +# print(f"{key} : {val},") +# hots to hots_simple +# dict["unique_name"] = [idxs] +# hots_to_cat = {} +# for class_idx, class_name in enumerate(hots_v1_classes()): +# class_cat_name = class_name.split("_")[0] +# if "juice_box" in class_name: +# class_cat_name = "juice_box" +# if "background" in class_name: +# class_cat_name = "_background_" +# if class_cat_name not in hots_to_cat.keys(): +# hots_to_cat[class_cat_name] = [] +# hots_to_cat[class_cat_name].append(class_idx) + + +# for idx, (key, val) in enumerate(hots_to_cat.items()): + +# print(f"cat name {key}, idx {idx}:\n members: {[hots_v1_classes()[item] for item in val]}") + +# sum_ = sum(len(val) for key, val in hots_to_cat.items()) +# print(f"total classes accounted for: {sum_}") + + +# print("IRL CAT") +# irl_vision_to_cat = {} +# for class_idx, class_name in enumerate(irl_vision_sim_classes()): +# class_cat_name = class_name.split("_")[0] +# if "juice_box" in class_name: +# class_cat_name = "juice_box" +# if "background" in class_name: +# class_cat_name = "_background_" +# if class_cat_name not in irl_vision_to_cat.keys(): +# irl_vision_to_cat[class_cat_name] = [] +# irl_vision_to_cat[class_cat_name].append(class_idx) + + +# for idx, (key, val) in enumerate(irl_vision_to_cat.items()): + +# print(f"cat name {key}, idx {idx}:\n members: {[irl_vision_sim_classes()[item] for item in val]}") + +# sum_ = sum(len(val) for key, val in irl_vision_to_cat.items()) +# print(f"total classes accounted for: {sum_}") + + + +# print("\nkeys not found in hots: ") +# for idx_irl, (key_irl, val_irl) in enumerate(irl_vision_to_cat.items()): +# if not key_irl in hots_to_cat.keys(): +# print(key_irl) + +# print(f"\n keys not found in irl_vision") +# for key, val in hots_to_cat.items(): +# if key not in irl_vision_to_cat.keys(): +# print(key) + + +# print(f"len irl,hots: {len(irl_vision_to_cat)}, {len(hots_to_cat)}") + + +# #### HERE THE DICTS ARE CREATED #### +# hots2hots_cat = {} +# for cat_idx, (cat_name, item_idx_list) in enumerate(hots_to_cat.items()): +# for item_idx in item_idx_list: +# hots2hots_cat[item_idx] = cat_idx + +# irl_vision2irl_vision_cat = {} +# for cat_idx, (cat_name, item_idx_list) in enumerate(irl_vision_to_cat.items()): +# for item_idx in item_idx_list: +# irl_vision2irl_vision_cat[item_idx] = cat_idx + +# irl_vision_cat2hots_cat = {} +# for idx_irl_cat, (name_irl_cat, item_idx_list_irl) in enumerate(irl_vision_to_cat.items()): +# for idx_hots_cat, (name_hots_cat, item_idx_list_hots) in enumerate(hots_to_cat.items()): +# if name_irl_cat == name_hots_cat: +# irl_vision_cat2hots_cat[idx_irl_cat] = idx_hots_cat + +# hots_cat2irl_vision_cat = { +# val : key for key, val in irl_vision_cat2hots_cat.items() +# } +# print("idx map:\n") +# print("HOTS2HOTS_CAT = {") +# for item_idx, cat_idx in hots2hots_cat.items(): +# print(f"\t\t{item_idx} : {cat_idx},") +# print("}") + +# print("") + +# print("IRL_VISION2IRL_VISION_CAT = {") +# for item_idx, cat_idx in irl_vision2irl_vision_cat.items(): +# print(f"\t\t{item_idx} : {cat_idx},") +# print("}") + +# print("") +# print("IRL_VISION_CAT2HOTS_CAT = {") +# for item_idx, cat_idx in irl_vision_cat2hots_cat.items(): +# print(f"\t\t{item_idx} : {cat_idx},") +# print("}") + +# print("") +# print("HOTS_CAT2IRL_VISION_CAT = {") +# for item_idx, cat_idx in hots_cat2irl_vision_cat.items(): +# print(f"\t\t{item_idx} : {cat_idx},") +# print("}") \ No newline at end of file diff --git a/my_projects/conversion_tests/scripts/organise_conversion_data.py b/my_projects/conversion_tests/scripts/organise_conversion_data.py new file mode 100644 index 0000000000..958ced1c4e --- /dev/null +++ b/my_projects/conversion_tests/scripts/organise_conversion_data.py @@ -0,0 +1,415 @@ +import os +import json +import argparse +from copy import deepcopy +from my_projects.conversion_tests.converters.conversion_dicts import ( + get_missing_class_names +) +from my_projects.scripts.generate_latex_tables import ( + per_label_per_model as gen_latex, + load_json_file as load_json, + save_dict_as_json as save_json +) +from my_projects.scripts.plot_per_label_results import ( + make_class_hist, + save_class_hist_plot +) + +from my_projects.scripts.plotting_utils import map_dataset_name +from my_projects.scripts.generate_json_all_test_results import ( + generate_confusion_dict_dataset as gen_new_conf_dict +) + +from my_projects.scripts.clean_json_for_reporting import fix_jsons + +import subprocess + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + 'conversion_path', + type=str + ) + + parser.add_argument( + '--all', + '-a', + action='store_true' + ) + + parser.add_argument( + '--source', + type=str, + default=None + ) + parser.add_argument( + '--target', + type=str, + default=None + ) + + parser.add_argument( + '--gen_table', + '-gt', + action='store_true' + ) + parser.add_argument( + '--gen_plot', + '-gp', + action='store_true' + ) + parser.add_argument( + '--gen_confusion_mat', + '-gcm', + action='store_true' + ) + + parser.add_argument( + '--confusion_mat_file_name', + '-cmfn', + type=str, + default=None + ) + parser.add_argument( + '--per_label_file_name', + '-plfn', + type=str, + default=None + ) + parser.add_argument( + '--global_results_file_name', + '-grfn', + type=str, + default=None + ) + + args = parser.parse_args() + if args.all: + return args + if args.conversion_path[-1] == "/": + args.conversion_path = args.conversion_path[:-1] + + _, conversion_name = os.path.split(args.conversion_path) + + if args.source is None or args.target is None: + + [args.source, args.target] = conversion_name.upper().split("2") + + if not (args.gen_table or args.gen_plot or args.gen_confusion_mat): + args.gen_table = True + args.gen_plot = True + args.gen_confusion_mat = True + + if args.confusion_mat_file_name is None: + data_dir_path = os.path.join( + args.conversion_path, + "data" + ) + args.confusion_mat_file_name = [ + file_name for file_name in os.listdir(data_dir_path) + if "confusion_top_5.json" in file_name + ][0] + if args.per_label_file_name is None: + args.per_label_file_name = f"{conversion_name}_per_label_results.json" + if args.global_results_file_name is None: + args.global_results_file_name = f"{conversion_name}_results.json" + + return args + +# result dict is any dict with class names as keys +def mark_unkown_labels( + label_data: dict, + unknown_labels: list +) -> dict: + fixed_dict = {} + for class_label, value in label_data.items(): + if class_label in unknown_labels: + class_label = f"*{class_label}" + fixed_dict[class_label] = value + return fixed_dict + + +# result dict is any dict with class names as keys +def remove_unknown_labels( + label_data: dict, + unknown_labels: list +) -> dict: + fixed_dict = {} + for class_label, value in label_data.items(): + if class_label in unknown_labels: + continue + fixed_dict[class_label] = value + return fixed_dict + + +def make_marked_confusion_matrix( + args, + conf_matrix: dict, + unkown_labels: list, + save_app = "" +): + new_matrix = deepcopy(conf_matrix) + for model_name, conf_list in new_matrix.items(): + for conf_item in conf_list: + for key, value in conf_item.items(): + if value in unkown_labels: + conf_item[key] = f"*{value}" + + save_file_name = args.confusion_mat_file_name.replace( + ".json", + f"{save_app}.json" + ) + + save_path = os.path.join( + args.conversion_path, + "data", + save_file_name + ) + save_json( + data_dict=new_matrix, + dump_file_path=save_path + ) + return new_matrix + +def make_marked_per_label_results( + args, + global_per_label_results: dict, + unknown_labels: list +): + fixed_label_results = {} + for model_name, model_label_data in global_per_label_results.items(): + fixed_model_data = mark_unkown_labels( + label_data=model_label_data, + unknown_labels=unknown_labels + ) + fixed_label_results[model_name] = fixed_model_data + return fixed_label_results + +def plot_per_class_data( + args, + fixed_per_label_results: dict, + save_app = "" +): + + for model_name, model_label_data in fixed_per_label_results.items(): + model_class_hist = make_class_hist( + model_data_dict=model_label_data, + metric="IoU" + ) + model_plot_save_path = os.path.join( + args.conversion_path, + "data", + f"{model_name.lower()}_per_label_hist{save_app}" + ) + save_class_hist_plot( + class_hist=model_class_hist, + save_path=model_plot_save_path + ) + +def gen_per_class_table( + args, + fixed_per_label_results: dict, + save_app = "" +): + table_file_path = os.path.join( + args.conversion_path, + "data", + f"latex_per_label_table{save_app}.txt" + ) + source = map_dataset_name(ds_name=args.source) + target = map_dataset_name(ds_name=args.target) + dataset_name = f"{source} to {target}" + gen_latex( + label_results_dict=fixed_per_label_results, + dataset_name=dataset_name, + file_path=table_file_path + ) + + +def get_metrics(global_per_label_results): + for model_name, model_label_data in global_per_label_results.items(): + for label, metrics in model_label_data.items(): + return list(metrics.keys()) + + +def fix_global_data( + args, + global_per_label_results: dict, + unknown_labels: list, + save_app = "", + n_decimals = 1 +): + metrics = get_metrics(global_per_label_results=global_per_label_results) + global_average_results = { + model_name : { + metric : 0 for metric in metrics + } for model_name in global_per_label_results.keys() + } + for model_name, model_label_data in global_per_label_results.items(): + fixed_model_label_data = remove_unknown_labels( + label_data=model_label_data, + unknown_labels=unknown_labels + ) + # sum all metrics for this model + for label, metric_dict in fixed_model_label_data.items(): + for metric, metric_value in metric_dict.items(): + global_average_results[model_name][metric] += metric_value + # average results + n_labels = len(fixed_model_label_data.items()) + for metric, metric_value in global_average_results[model_name].items(): + av_metric_value = round(metric_value / n_labels, n_decimals) + global_average_results[model_name][metric] = av_metric_value + + save_file_name = args.global_results_file_name.replace( + ".json", + f"{save_app}.json" + ) + + save_path = os.path.join( + args.conversion_path, + "data", + save_file_name + ) + save_json( + data_dict=global_average_results, + dump_file_path=save_path + ) + +def organise_all(args): + # in this case the entire test_result dir + test_results_path = args.conversion_path + + for results_dir in os.listdir(test_results_path): + results_dir_path = os.path.join( + test_results_path, + results_dir + ) + subprocess.call( + [ + "python", + "my_projects/conversion_tests/scripts/organise_conversion_data.py", + results_dir_path + ] + ) + +def clean_all(args): + # in this case the entire test_result dir + test_results_path = args.conversion_path + for results_dir in os.listdir(test_results_path): + results_dir_path = os.path.join( + test_results_path, + results_dir + ) + data_path = os.path.join( + results_dir_path, + "data" + ) + for file_name in os.listdir(data_path): + if "marked" in file_name: + os.remove( + os.path.join( + data_path, + file_name + ) + ) + return + +def temp_fix(args): + # in this case the entire test_result dir + test_results_path = args.conversion_path + # res dir being a ds + for results_dir in os.listdir(test_results_path): + results_dir_path = os.path.join( + test_results_path, + results_dir + ) + conf_dict = gen_new_conf_dict( + dataset_path=results_dir_path, + n_confusion_values=5, + ignore_background=True + ) + conf_path = os.path.join( + results_dir_path, + "data", + f"{results_dir}_confusion_top_5.json" + ) + save_json( + data_dict=conf_dict, + dump_file_path=conf_path + ) + fix_jsons( + confusion_json_path=conf_path + ) + +def main(): + args = parse_args() + print(args) + + # return temp_fix(args=args) + + if args.all: + return organise_all(args=args) + + per_label_file_path = os.path.join( + args.conversion_path, + "data", + args.per_label_file_name + ) + + confusion_matrix_path = os.path.join( + args.conversion_path, + "data", + args.confusion_mat_file_name + ) + + + global_per_label_results = load_json( + json_file_path=per_label_file_path + ) + unknown_labels = get_missing_class_names( + source_dataset=args.source, + target_dataset=args.target + ) + save_app = "_marked" if unknown_labels else "" + print(f"unknown_labels : {unknown_labels}") + print(f"source, target: {args.source}, {args.target}") + + marked_per_label_results = make_marked_per_label_results( + args=args, + global_per_label_results=global_per_label_results, + unknown_labels=unknown_labels + ) + if args.gen_table: + gen_per_class_table( + args=args, + fixed_per_label_results=marked_per_label_results, + save_app=save_app + ) + if args.gen_plot: + plot_per_class_data( + args=args, + fixed_per_label_results=marked_per_label_results, + save_app=save_app + ) + # noting to add + if not unknown_labels: + return + if args.gen_confusion_mat: + confusion_matrix = load_json( + json_file_path=confusion_matrix_path + ) + make_marked_confusion_matrix( + args=args, + conf_matrix=confusion_matrix, + unkown_labels=unknown_labels, + save_app=save_app + ) + + fix_global_data( + args=args, + global_per_label_results=global_per_label_results, + unknown_labels=unknown_labels, + save_app=save_app + ) +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/my_projects/conversion_tests/selection_trained/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/20241107_100923/vis_data/20241107_100923.json b/my_projects/conversion_tests/selection_trained/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/20241107_100923/vis_data/20241107_100923.json new file mode 100644 index 0000000000..b87f7144ac --- /dev/null +++ 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a/my_projects/conversion_tests/selection_trained/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/20241107_100923/vis_data/config.py b/my_projects/conversion_tests/selection_trained/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/20241107_100923/vis_data/config.py new file mode 100644 index 0000000000..6ecb6abf7a --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/20241107_100923/vis_data/config.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512' diff --git 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b/my_projects/conversion_tests/selection_trained/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_102748/vis_data/20241107_102748.json b/my_projects/conversion_tests/selection_trained/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_102748/vis_data/20241107_102748.json new file mode 100644 index 0000000000..406185768d --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_102748/vis_data/20241107_102748.json @@ -0,0 +1,210 @@ +{"base_lr": 9.955884763429715e-05, "lr": 9.955884763429715e-06, "data_time": 0.005306100845336914, "grad_norm": 275.26212310791016, "loss": 25.957440185546876, "decode.loss_cls": 0.6028118401765823, "decode.loss_mask": 0.4600101500749588, "decode.loss_dice": 0.7139581501483917, "decode.d0.loss_cls": 7.076222896575928, "decode.d0.loss_mask": 0.7174189567565918, "decode.d0.loss_dice": 1.1237005084753036, "decode.d1.loss_cls": 0.6498780250549316, "decode.d1.loss_mask": 0.6193289458751678, "decode.d1.loss_dice": 0.8730206161737442, "decode.d2.loss_cls": 0.634550815820694, "decode.d2.loss_mask": 0.541669151186943, "decode.d2.loss_dice": 0.7963675856590271, "decode.d3.loss_cls": 0.6315273612737655, "decode.d3.loss_mask": 0.5092857867479325, "decode.d3.loss_dice": 0.7520143121480942, 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+auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_102748/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_102748/vis_data/scalars.json new file mode 100644 index 0000000000..406185768d --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_102748/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 9.955884763429715e-05, "lr": 9.955884763429715e-06, "data_time": 0.005306100845336914, "grad_norm": 275.26212310791016, "loss": 25.957440185546876, "decode.loss_cls": 0.6028118401765823, "decode.loss_mask": 0.4600101500749588, "decode.loss_dice": 0.7139581501483917, "decode.d0.loss_cls": 7.076222896575928, 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a/my_projects/conversion_tests/selection_trained/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512.py b/my_projects/conversion_tests/selection_trained/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512.py new file mode 100644 index 0000000000..e6a35be62c --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/20241107_113810.json b/my_projects/conversion_tests/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/20241107_113810.json new file mode 100644 index 0000000000..0ededb5788 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/20241107_113810.json @@ -0,0 +1,210 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 0.005614161491394043, "grad_norm": 518.7013122558594, "loss": 22.32619705200195, "decode.loss_cls": 3.158992862701416, "decode.loss_mask": 0.20419039502739905, "decode.loss_dice": 0.4701965257525444, "decode.d0.loss_cls": 2.833039093017578, "decode.d0.loss_mask": 0.19366986453533172, "decode.d0.loss_dice": 0.46704602241516113, "decode.d1.loss_cls": 2.9835837125778197, "decode.d1.loss_mask": 0.1987227037549019, "decode.d1.loss_dice": 0.48906650245189665, "decode.d2.loss_cls": 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a/my_projects/conversion_tests/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/config.py b/my_projects/conversion_tests/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/config.py new file mode 100644 index 0000000000..2d10c53a59 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/config.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/scalars.json new file mode 100644 index 0000000000..0ededb5788 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_113810/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 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b/my_projects/conversion_tests/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_122327/vis_data/20241107_122327.json b/my_projects/conversion_tests/selection_trained/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_122327/vis_data/20241107_122327.json new file mode 100644 index 0000000000..c47556136f --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_122327/vis_data/20241107_122327.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.0035311222076416016, "loss": 3.044075846672058, "decode.loss_ce": 3.044075846672058, "decode.acc_seg": 5.998446941375732, "time": 0.10401318073272706, "iter": 50, "memory": 2068, "step": 50} +{"base_lr": 3.962697798532355e-06, "lr": 3.962697798532355e-06, "data_time": 0.003507566452026367, "loss": 2.8921288728713987, "decode.loss_ce": 2.8921288728713987, "decode.acc_seg": 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0.018054110162398395, "step": 10000} diff --git a/my_projects/conversion_tests/selection_trained/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_122327/vis_data/config.py b/my_projects/conversion_tests/selection_trained/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_122327/vis_data/config.py new file mode 100644 index 0000000000..0e1b1519e4 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_122327/vis_data/config.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_arid10_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_122327/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_122327/vis_data/scalars.json new file mode 100644 index 0000000000..c47556136f --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_122327/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.0035311222076416016, "loss": 3.044075846672058, "decode.loss_ce": 3.044075846672058, "decode.acc_seg": 5.998446941375732, "time": 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bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_arid10_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/20241107_124022.json b/my_projects/conversion_tests/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/20241107_124022.json new file mode 100644 index 0000000000..8fe9a66cbd --- /dev/null +++ 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'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/scalars.json new file mode 100644 index 0000000000..8fe9a66cbd --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_124022/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.005095911026000976, "loss": 2.9879141569137575, "decode.loss_ce": 2.9879141569137575, "decode.acc_seg": 0.021314797922968864, "time": 0.15326998233795167, "iter": 50, "memory": 3230, "step": 50} +{"base_lr": 3.962697798532355e-06, "lr": 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0.02160899428760304, "step": 10000} diff --git a/my_projects/conversion_tests/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512.py b/my_projects/conversion_tests/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512.py new file mode 100644 index 0000000000..f3edc434db --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20240917_114220/vis_data/20240917_114220.json b/my_projects/conversion_tests/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20240917_114220/vis_data/20240917_114220.json new file mode 100644 index 0000000000..8708068d5d --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20240917_114220/vis_data/20240917_114220.json @@ -0,0 +1,210 @@ +{"lr": 0.0006470980653769185, "data_time": 0.0023443222045898436, "loss": 3.194075566530228, "decode.loss_ce": 1.0545195966959, "decode.acc_seg": 58.623165130615234, "aux_0.loss_ce": 1.0397097498178482, "aux_0.acc_seg": 58.688602447509766, "aux_1.loss_ce": 1.0998461604118348, "aux_1.acc_seg": 58.62216567993164, "time": 0.09662184715270997, "iter": 50, "memory": 2548, 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512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20240917_114220/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20240917_114220/vis_data/scalars.json new file mode 100644 index 0000000000..8708068d5d --- /dev/null +++ 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95.96538543701172, "time": 0.10360825061798096, "iter": 9800, "memory": 834, "step": 9800} +{"lr": 0.00022949326073597528, "data_time": 0.002600884437561035, "loss": 0.1724634848535061, "decode.loss_ce": 0.04626269619911909, "decode.acc_seg": 98.75621795654297, "aux_0.loss_ce": 0.053839979507029054, "aux_0.acc_seg": 98.68860626220703, "aux_1.loss_ce": 0.07236080840229989, "aux_1.acc_seg": 98.06421661376953, "time": 0.10445232391357422, "iter": 9850, "memory": 834, "step": 9850} +{"lr": 0.00018990110580931495, "data_time": 0.0025274276733398436, "loss": 0.1779639169573784, "decode.loss_ce": 0.04599137771874666, "decode.acc_seg": 97.7010269165039, "aux_0.loss_ce": 0.05783211346715689, "aux_0.acc_seg": 97.28289031982422, "aux_1.loss_ce": 0.07414042465388775, "aux_1.acc_seg": 95.58478546142578, "time": 0.1042515754699707, "iter": 9900, "memory": 834, "step": 9900} +{"lr": 0.0001481768097295456, "data_time": 0.0024604558944702148, "loss": 0.1762592613697052, "decode.loss_ce": 0.04662298783659935, "decode.acc_seg": 97.66197967529297, "aux_0.loss_ce": 0.054077398777008054, "aux_0.acc_seg": 97.34232330322266, "aux_1.loss_ce": 0.07555887401103974, "aux_1.acc_seg": 95.97384643554688, "time": 0.1038219690322876, "iter": 9950, "memory": 834, "step": 9950} +{"lr": 0.0001, "data_time": 0.002491188049316406, "loss": 0.1734619840979576, "decode.loss_ce": 0.04582474865019322, "decode.acc_seg": 98.4322738647461, "aux_0.loss_ce": 0.050102119147777555, "aux_0.acc_seg": 98.27664184570312, "aux_1.loss_ce": 0.07753511779010296, "aux_1.acc_seg": 97.69593811035156, "time": 0.10485968589782715, "iter": 10000, "memory": 834, "step": 10000} +{"aAcc": 98.04, "mIoU": 70.63, "mAcc": 79.17, "data_time": 0.0014096999168395996, "time": 0.013367478847503661, "step": 10000} diff --git a/my_projects/conversion_tests/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/conversion_tests/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..bff6f6bba1 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/conv_cfg/bisenetv1_arid10_arid20_arid10.py b/my_projects/conversion_tests/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/conv_cfg/bisenetv1_arid10_arid20_arid10.py new file mode 100644 index 0000000000..87e62a4c6d --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/conv_cfg/bisenetv1_arid10_arid20_arid10.py @@ -0,0 +1,340 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='ARID20', + target_dataset='ARID10', + test_dataset='ARID10', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ARID20', + target_dataset='ARID10', + test_dataset='ARID10', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_161558/vis_data/20240917_161558.json b/my_projects/conversion_tests/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_161558/vis_data/20240917_161558.json new file mode 100644 index 0000000000..b19363571b --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_161558/vis_data/20240917_161558.json @@ -0,0 +1,210 @@ +{"base_lr": 9.955884763429715e-05, "lr": 9.955884763429715e-06, 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b/my_projects/conversion_tests/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_161558/vis_data/config.py @@ -0,0 +1,435 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_161558/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_161558/vis_data/scalars.json new file mode 100644 index 0000000000..b19363571b --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_161558/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 9.955884763429715e-05, "lr": 9.955884763429715e-06, "data_time": 0.005344867706298828, "grad_norm": 194.01115036010742, "loss": 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0.18552006930112838, "decode.d8.loss_cls": 0.011097147088003112, "decode.d8.loss_mask": 0.11716443970799446, "decode.d8.loss_dice": 0.17722982168197632, "time": 0.4180600166320801, "iter": 10000, "memory": 2995, "step": 10000} +{"aAcc": 98.93, "mIoU": 85.96, "mAcc": 91.65, "data_time": 0.0014711809158325195, "time": 0.07034803152084351, "step": 10000} diff --git a/my_projects/conversion_tests/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/conv_cfg/mask2former_arid10_arid20_arid10.py b/my_projects/conversion_tests/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/conv_cfg/mask2former_arid10_arid20_arid10.py new file mode 100644 index 0000000000..e525c90ab5 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/conv_cfg/mask2former_arid10_arid20_arid10.py @@ -0,0 +1,439 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='ARID20', + target_dataset='ARID10', + test_dataset='ARID10', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ARID20', + target_dataset='ARID10', + test_dataset='ARID10', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/conversion_tests/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..f18f3142a6 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/20240917_172715.json b/my_projects/conversion_tests/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/20240917_172715.json new file mode 100644 index 0000000000..198798d077 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/20240917_172715.json @@ -0,0 +1,210 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 0.0051671028137207035, "grad_norm": 470.30755310058595, "loss": 23.879179000854492, "decode.loss_cls": 3.3874343633651733, "decode.loss_mask": 0.17753420174121856, "decode.loss_dice": 0.3346044570207596, "decode.d0.loss_cls": 3.4928240299224855, "decode.d0.loss_mask": 0.17159825190901756, "decode.d0.loss_dice": 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b/my_projects/conversion_tests/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/config.py new file mode 100644 index 0000000000..df7a8dc4f0 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/config.py @@ -0,0 +1,525 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/scalars.json new file mode 100644 index 0000000000..198798d077 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_172715/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 0.0051671028137207035, "grad_norm": 470.30755310058595, "loss": 23.879179000854492, 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6.001200240048298e-07, "lr": 6.001200240048298e-07, "data_time": 0.005232763290405273, "grad_norm": 7.259053719043732, "loss": 0.671440252661705, "decode.loss_cls": 0.028161310061113907, "decode.loss_mask": 0.024675468727946282, "decode.loss_dice": 0.056134984269738196, "decode.d0.loss_cls": 0.03515402803895995, "decode.d0.loss_mask": 0.024774455279111863, "decode.d0.loss_dice": 0.05615993998944759, "decode.d1.loss_cls": 0.03431595647707582, "decode.d1.loss_mask": 0.024231760017573833, "decode.d1.loss_dice": 0.055811844393610956, "decode.d2.loss_cls": 0.030676780745852738, "decode.d2.loss_mask": 0.024133421247825027, "decode.d2.loss_dice": 0.055426908656954765, "decode.d3.loss_cls": 0.02982150013122009, "decode.d3.loss_mask": 0.024430577363818883, "decode.d3.loss_dice": 0.0555012509226799, "decode.d4.loss_cls": 0.031683862702629996, "decode.d4.loss_mask": 0.0246188810095191, "decode.d4.loss_dice": 0.055727294459939, "time": 0.2633273363113403, "iter": 9950, "memory": 3066, "step": 9950} +{"base_lr": 0.0, "lr": 0.0, "data_time": 0.005176520347595215, "grad_norm": 5.6687327980995175, "loss": 0.5725176945328713, "decode.loss_cls": 0.014588405531321768, "decode.loss_mask": 0.025924877636134623, "decode.loss_dice": 0.05534221697598696, "decode.d0.loss_cls": 0.016716441420430782, "decode.d0.loss_mask": 0.027155735064297915, "decode.d0.loss_dice": 0.05359881930053234, "decode.d1.loss_cls": 0.015642854746693045, "decode.d1.loss_mask": 0.02589314989745617, "decode.d1.loss_dice": 0.05301286745816469, "decode.d2.loss_cls": 0.014333615126815857, "decode.d2.loss_mask": 0.025957035832107067, "decode.d2.loss_dice": 0.05428182240575552, "decode.d3.loss_cls": 0.014675870265637058, "decode.d3.loss_mask": 0.025721714459359647, "decode.d3.loss_dice": 0.05478774532675743, "decode.d4.loss_cls": 0.014244628862434184, "decode.d4.loss_mask": 0.02554449802264571, "decode.d4.loss_dice": 0.05509538426995277, "time": 0.26356964111328124, "iter": 10000, "memory": 3067, "step": 10000} +{"aAcc": 98.95, "mIoU": 87.52, "mAcc": 93.06, "data_time": 0.0014193153381347656, "time": 0.04673076629638672, "step": 10000} diff --git a/my_projects/conversion_tests/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/conv_cfg/maskformer_arid10_arid20_arid10.py b/my_projects/conversion_tests/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/conv_cfg/maskformer_arid10_arid20_arid10.py new file mode 100644 index 0000000000..b9fd6b01b3 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/conv_cfg/maskformer_arid10_arid20_arid10.py @@ -0,0 +1,529 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='ARID20', + target_dataset='ARID10', + test_dataset='ARID10', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ARID20', + target_dataset='ARID10', + test_dataset='ARID10', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/conversion_tests/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..e07ae267ce --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/20240917_120056/vis_data/20240917_120056.json b/my_projects/conversion_tests/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/20240917_120056/vis_data/20240917_120056.json new file mode 100644 index 0000000000..bb247aadd2 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/20240917_120056/vis_data/20240917_120056.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.0033666133880615235, "loss": 3.074103283882141, "decode.loss_ce": 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seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/20240917_120056/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/20240917_120056/vis_data/scalars.json new file mode 100644 index 0000000000..bb247aadd2 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/20240917_120056/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, 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98.78278350830078, "time": 0.09546680450439453, "iter": 9600, "memory": 1038, "step": 9600} +{"base_lr": 2.470878926932549e-06, "lr": 2.470878926932549e-06, "data_time": 0.0033743619918823243, "loss": 0.043848749436438086, "decode.loss_ce": 0.043848749436438086, "decode.acc_seg": 98.86367797851562, "time": 0.09547820091247558, "iter": 9650, "memory": 1038, "step": 9650} +{"base_lr": 2.117896223085038e-06, "lr": 2.117896223085038e-06, "data_time": 0.0033708810806274414, "loss": 0.027665875479578973, "decode.loss_ce": 0.027665875479578973, "decode.acc_seg": 98.2757568359375, "time": 0.09726934432983399, "iter": 9700, "memory": 1038, "step": 9700} +{"base_lr": 1.7649135192375295e-06, "lr": 1.7649135192375295e-06, "data_time": 0.003361964225769043, "loss": 0.04665385689586401, "decode.loss_ce": 0.04665385689586401, "decode.acc_seg": 98.71662902832031, "time": 0.095601487159729, "iter": 9750, "memory": 1038, "step": 9750} +{"base_lr": 1.411930815390025e-06, "lr": 1.411930815390025e-06, "data_time": 0.003420448303222656, "loss": 0.038175484910607335, "decode.loss_ce": 0.038175484910607335, "decode.acc_seg": 98.30484771728516, "time": 0.0995938539505005, "iter": 9800, "memory": 1039, "step": 9800} +{"base_lr": 1.0589481115425196e-06, "lr": 1.0589481115425196e-06, "data_time": 0.003345298767089844, "loss": 0.029713648557662963, "decode.loss_ce": 0.029713648557662963, "decode.acc_seg": 98.99406433105469, "time": 0.0975881576538086, "iter": 9850, "memory": 1038, "step": 9850} +{"base_lr": 7.059654076950137e-07, "lr": 7.059654076950137e-07, "data_time": 0.003276562690734863, "loss": 0.04301054812967777, "decode.loss_ce": 0.04301054812967777, "decode.acc_seg": 99.54662322998047, "time": 0.09666247367858886, "iter": 9900, "memory": 1039, "step": 9900} +{"base_lr": 3.529827038475068e-07, "lr": 3.529827038475068e-07, "data_time": 0.0033962488174438476, "loss": 0.0370460532605648, "decode.loss_ce": 0.0370460532605648, "decode.acc_seg": 98.11743927001953, "time": 0.0980971097946167, "iter": 9950, "memory": 1039, "step": 9950} +{"base_lr": 0.0, "lr": 0.0, "data_time": 0.0033989191055297852, "loss": 0.03020934252999723, "decode.loss_ce": 0.03020934252999723, "decode.acc_seg": 98.68294525146484, "time": 0.09637930393218994, "iter": 10000, "memory": 1038, "step": 10000} +{"aAcc": 98.41, "mIoU": 81.3, "mAcc": 89.3, "data_time": 0.0014070439338684081, "time": 0.01824004888534546, "step": 10000} diff --git a/my_projects/conversion_tests/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/conv_cfg/segformer_arid10_arid20_arid10.py b/my_projects/conversion_tests/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/conv_cfg/segformer_arid10_arid20_arid10.py new file mode 100644 index 0000000000..3141066f81 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/conv_cfg/segformer_arid10_arid20_arid10.py @@ -0,0 +1,326 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='ARID20', + target_dataset='ARID10', + test_dataset='ARID10', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ARID20', + target_dataset='ARID10', + test_dataset='ARID10', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/conversion_tests/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..0d9ad30ef8 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 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a/my_projects/conversion_tests/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/config.py b/my_projects/conversion_tests/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/config.py new file mode 100644 index 0000000000..a8f9dbb8fa --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/config.py @@ -0,0 +1,344 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/scalars.json new file mode 100644 index 0000000000..cc70a96776 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240917_121739/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.004879283905029297, "loss": 3.111197996139526, "decode.loss_ce": 3.111197996139526, "decode.acc_seg": 58.2942008972168, "time": 0.15078768730163575, "iter": 50, "memory": 3230, "step": 50} +{"base_lr": 3.962697798532355e-06, "lr": 3.962697798532355e-06, "data_time": 0.004775619506835938, "loss": 2.777503323554993, "decode.loss_ce": 2.777503323554993, "decode.acc_seg": 81.89331817626953, "time": 0.15137345790863038, "iter": 100, "memory": 1345, "step": 100} +{"base_lr": 5.964030020013346e-06, "lr": 5.964030020013346e-06, 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b/my_projects/conversion_tests/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/conv_cfg/segnext_arid10_arid20_arid10.py @@ -0,0 +1,348 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='ARID20', + target_dataset='ARID10', + test_dataset='ARID10', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ARID20', + target_dataset='ARID10', + test_dataset='ARID10', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/conversion_tests/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..344a86a610 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/20240805_022137/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/20240805_022137/vis_data/scalars.json new file mode 100644 index 0000000000..cf34e7d0a9 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/20240805_022137/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 21.96} +{"step": 2000, "mIoU": 49.14} +{"step": 3000, "mIoU": 60.57} +{"step": 4000, "mIoU": 64.15} +{"step": 5000, "mIoU": 65.96} +{"step": 6000, "mIoU": 70.07} +{"step": 7000, "mIoU": 71.17} +{"step": 8000, "mIoU": 72.4} +{"step": 9000, "mIoU": 75.0} +{"step": 10000, "mIoU": 76.82} diff --git a/my_projects/conversion_tests/selection_trained/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512.py b/my_projects/conversion_tests/selection_trained/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..c1210fe31e --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/conv_cfg/bisenetv1_hots_cat_hots_hots_cat.py b/my_projects/conversion_tests/selection_trained/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/conv_cfg/bisenetv1_hots_cat_hots_hots_cat.py new file mode 100644 index 0000000000..d338746c19 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/conv_cfg/bisenetv1_hots_cat_hots_hots_cat.py @@ -0,0 +1,341 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='HOTS', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/conv_cfg/bisenetv1_irl_vision_cat_hots_irl_vision_cat.py b/my_projects/conversion_tests/selection_trained/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/conv_cfg/bisenetv1_irl_vision_cat_hots_irl_vision_cat.py new file mode 100644 index 0000000000..48016cc9ad --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/conv_cfg/bisenetv1_irl_vision_cat_hots_irl_vision_cat.py @@ -0,0 +1,341 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='HOTS', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_031129/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_031129/vis_data/scalars.json new file mode 100644 index 0000000000..f13abf5355 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_031129/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 65.54} +{"step": 2000, "mIoU": 70.33} +{"step": 3000, "mIoU": 77.32} +{"step": 4000, "mIoU": 79.43} +{"step": 5000, "mIoU": 81.82} +{"step": 6000, "mIoU": 83.13} +{"step": 7000, "mIoU": 85.55} +{"step": 8000, "mIoU": 79.95} +{"step": 9000, "mIoU": 84.92} +{"step": 10000, "mIoU": 82.69} diff --git a/my_projects/conversion_tests/selection_trained/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/conv_cfg/mask2former_hots_cat_hots_hots_cat.py b/my_projects/conversion_tests/selection_trained/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/conv_cfg/mask2former_hots_cat_hots_hots_cat.py new file mode 100644 index 0000000000..6ca975c858 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/conv_cfg/mask2former_hots_cat_hots_hots_cat.py @@ -0,0 +1,455 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='HOTS', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/conv_cfg/mask2former_irl_vision_cat_hots_irl_vision_cat.py b/my_projects/conversion_tests/selection_trained/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/conv_cfg/mask2former_irl_vision_cat_hots_irl_vision_cat.py new file mode 100644 index 0000000000..a4d45e5029 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/conv_cfg/mask2former_irl_vision_cat_hots_irl_vision_cat.py @@ -0,0 +1,455 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='HOTS', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512.py b/my_projects/conversion_tests/selection_trained/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..aac4b92191 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512.py @@ -0,0 +1,447 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_052833/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_052833/vis_data/scalars.json new file mode 100644 index 0000000000..0c7d61fbe9 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_052833/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 6.35} +{"step": 2000, "mIoU": 30.41} +{"step": 3000, "mIoU": 50.1} +{"step": 4000, "mIoU": 67.93} +{"step": 5000, "mIoU": 69.17} +{"step": 6000, "mIoU": 74.48} +{"step": 7000, "mIoU": 77.79} +{"step": 8000, "mIoU": 80.74} +{"step": 9000, "mIoU": 81.36} +{"step": 10000, "mIoU": 78.16} diff --git a/my_projects/conversion_tests/selection_trained/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/conv_cfg/maskformer_hots_cat_hots_hots_cat.py b/my_projects/conversion_tests/selection_trained/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/conv_cfg/maskformer_hots_cat_hots_hots_cat.py new file mode 100644 index 0000000000..d56bab860d --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/conv_cfg/maskformer_hots_cat_hots_hots_cat.py @@ -0,0 +1,545 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='HOTS', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/conv_cfg/maskformer_irl_vision_cat_hots_irl_vision_cat.py b/my_projects/conversion_tests/selection_trained/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/conv_cfg/maskformer_irl_vision_cat_hots_irl_vision_cat.py new file mode 100644 index 0000000000..b9d292b0f8 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/conv_cfg/maskformer_irl_vision_cat_hots_irl_vision_cat.py @@ -0,0 +1,545 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='HOTS', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512.py b/my_projects/conversion_tests/selection_trained/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..4a1e0f9c65 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512.py @@ -0,0 +1,537 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/20240805_065747/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/20240805_065747/vis_data/scalars.json new file mode 100644 index 0000000000..168b26a328 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/20240805_065747/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 27.4} +{"step": 2000, "mIoU": 66.87} +{"step": 3000, "mIoU": 72.3} +{"step": 4000, "mIoU": 75.33} +{"step": 5000, "mIoU": 75.85} +{"step": 6000, "mIoU": 75.37} +{"step": 7000, "mIoU": 77.84} +{"step": 8000, "mIoU": 78.78} +{"step": 9000, "mIoU": 79.08} +{"step": 10000, "mIoU": 78.79} diff --git a/my_projects/conversion_tests/selection_trained/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/conv_cfg/segformer_hots_cat_hots_hots_cat.py b/my_projects/conversion_tests/selection_trained/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/conv_cfg/segformer_hots_cat_hots_hots_cat.py new file mode 100644 index 0000000000..5940c6dfcb --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/conv_cfg/segformer_hots_cat_hots_hots_cat.py @@ -0,0 +1,327 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='HOTS', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/conv_cfg/segformer_irl_vision_cat_hots_irl_vision_cat.py b/my_projects/conversion_tests/selection_trained/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/conv_cfg/segformer_irl_vision_cat_hots_irl_vision_cat.py new file mode 100644 index 0000000000..7566de965a --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/conv_cfg/segformer_irl_vision_cat_hots_irl_vision_cat.py @@ -0,0 +1,327 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='HOTS', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_mit-b0_1xb2-10k_hots-v1-512x512.py b/my_projects/conversion_tests/selection_trained/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_mit-b0_1xb2-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..3894a9cd63 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_mit-b0_1xb2-10k_hots-v1-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_073545/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_073545/vis_data/scalars.json new file mode 100644 index 0000000000..25dd462849 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/20240805_073545/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 24.39} +{"step": 2000, "mIoU": 67.3} +{"step": 3000, "mIoU": 75.59} +{"step": 4000, "mIoU": 78.33} +{"step": 5000, "mIoU": 80.14} +{"step": 6000, "mIoU": 80.8} +{"step": 7000, "mIoU": 84.13} +{"step": 8000, "mIoU": 83.16} +{"step": 9000, "mIoU": 82.34} +{"step": 10000, "mIoU": 81.75} diff --git a/my_projects/conversion_tests/selection_trained/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/conv_cfg/segnext_hots_cat_hots_hots_cat.py b/my_projects/conversion_tests/selection_trained/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/conv_cfg/segnext_hots_cat_hots_hots_cat.py new file mode 100644 index 0000000000..917d10aac0 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/conv_cfg/segnext_hots_cat_hots_hots_cat.py @@ -0,0 +1,349 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=47, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='HOTS', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/conv_cfg/segnext_irl_vision_cat_hots_irl_vision_cat.py b/my_projects/conversion_tests/selection_trained/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/conv_cfg/segnext_irl_vision_cat_hots_irl_vision_cat.py new file mode 100644 index 0000000000..3932ce23ef --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/conv_cfg/segnext_irl_vision_cat_hots_irl_vision_cat.py @@ -0,0 +1,349 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=47, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='HOTS', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512.py b/my_projects/conversion_tests/selection_trained/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..8d3bb82ac6 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=47, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/20240920_124652.json b/my_projects/conversion_tests/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/20240920_124652.json new file mode 100644 index 0000000000..06c9db4fe7 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/20240920_124652.json @@ -0,0 +1,211 @@ +{"lr": 0.0006200800533689131, "data_time": 0.002659726142883301, "loss": 2.9682978868484495, "decode.loss_ce": 0.9420152902603149, "decode.acc_seg": 78.80119323730469, "aux_0.loss_ce": 1.0499185621738434, "aux_0.acc_seg": 78.80119323730469, "aux_1.loss_ce": 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a/my_projects/conversion_tests/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/config.py b/my_projects/conversion_tests/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/config.py new file mode 100644 index 0000000000..5277c4bd18 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/config.py @@ -0,0 +1,335 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/scalars.json new file mode 100644 index 0000000000..06c9db4fe7 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240920_124652/vis_data/scalars.json @@ -0,0 +1,211 @@ +{"lr": 0.0006200800533689131, "data_time": 0.002659726142883301, "loss": 2.9682978868484495, "decode.loss_ce": 0.9420152902603149, "decode.acc_seg": 78.80119323730469, "aux_0.loss_ce": 1.0499185621738434, "aux_0.acc_seg": 78.80119323730469, "aux_1.loss_ce": 0.9763640403747559, 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a/my_projects/conversion_tests/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512.py b/my_projects/conversion_tests/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512.py new file mode 100644 index 0000000000..5277c4bd18 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512.py @@ -0,0 +1,335 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/conv_cfg/bisenetv1_irl_vision_cat_hots_cat_irl_vision_cat.py b/my_projects/conversion_tests/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/conv_cfg/bisenetv1_irl_vision_cat_hots_cat_irl_vision_cat.py new file mode 100644 index 0000000000..0c89e47800 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/conv_cfg/bisenetv1_irl_vision_cat_hots_cat_irl_vision_cat.py @@ -0,0 +1,343 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='HOTS_CAT', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS_CAT', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/20240920_130500.json b/my_projects/conversion_tests/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/20240920_130500.json new file mode 100644 index 0000000000..b4dc23be97 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/20240920_130500.json @@ -0,0 +1,211 @@ +{"base_lr": 9.963989187614348e-05, "lr": 9.963989187614342e-06, "data_time": 0.0056323051452636715, "grad_norm": 205.09661560058595, "loss": 22.58833866119385, "decode.loss_cls": 0.585413932800293, "decode.loss_mask": 0.4014120578765869, "decode.loss_dice": 0.5713946372270584, "decode.d0.loss_cls": 6.894965267181396, "decode.d0.loss_mask": 0.49705882370471954, "decode.d0.loss_dice": 0.8819123923778533, "decode.d1.loss_cls": 0.6922816574573517, "decode.d1.loss_mask": 0.38555236011743543, "decode.d1.loss_dice": 0.6555641055107116, "decode.d2.loss_cls": 0.6598462104797364, 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0.4434641122817993, "iter": 10000, "memory": 2950, "step": 10000} +{"aAcc": 98.98, "mIoU": 90.04, "mAcc": 93.6, "data_time": 0.0025933504104614256, "time": 0.07924876213073731, "step": 10000} diff --git a/my_projects/conversion_tests/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/config.py b/my_projects/conversion_tests/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/config.py new file mode 100644 index 0000000000..5dc6472aa4 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/config.py @@ -0,0 +1,428 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/scalars.json new file mode 100644 index 0000000000..b4dc23be97 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_130500/vis_data/scalars.json @@ -0,0 +1,211 @@ +{"base_lr": 9.963989187614348e-05, "lr": 9.963989187614342e-06, "data_time": 0.0056323051452636715, "grad_norm": 205.09661560058595, "loss": 22.58833866119385, "decode.loss_cls": 0.585413932800293, "decode.loss_mask": 0.4014120578765869, "decode.loss_dice": 0.5713946372270584, "decode.d0.loss_cls": 6.894965267181396, "decode.d0.loss_mask": 0.49705882370471954, "decode.d0.loss_dice": 0.8819123923778533, "decode.d1.loss_cls": 0.6922816574573517, 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b/my_projects/conversion_tests/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/conv_cfg/mask2former_irl_vision_cat_hots_cat_irl_vision_cat.py @@ -0,0 +1,436 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='HOTS_CAT', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS_CAT', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py b/my_projects/conversion_tests/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py new file mode 100644 index 0000000000..5dc6472aa4 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py @@ -0,0 +1,428 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/20240925_130957.json b/my_projects/conversion_tests/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/20240925_130957.json new file mode 100644 index 0000000000..e543e6b8c1 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/20240925_130957.json @@ -0,0 +1,211 @@ +{"base_lr": 4.801555391078211e-07, "lr": 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a/my_projects/conversion_tests/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/config.py b/my_projects/conversion_tests/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/config.py new file mode 100644 index 0000000000..585dc791fb --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/config.py @@ -0,0 +1,518 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/scalars.json new file mode 100644 index 0000000000..e543e6b8c1 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_130957/vis_data/scalars.json @@ -0,0 +1,211 @@ +{"base_lr": 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"decode.loss_dice": 0.04062049593776464, "decode.d0.loss_cls": 0.007335875289572869, "decode.d0.loss_mask": 0.019258128013461828, "decode.d0.loss_dice": 0.04081912934780121, "decode.d1.loss_cls": 0.0062700475886231285, "decode.d1.loss_mask": 0.018930184468626977, "decode.d1.loss_dice": 0.04057639706879854, "decode.d2.loss_cls": 0.005539317475631833, "decode.d2.loss_mask": 0.01890755109488964, "decode.d2.loss_dice": 0.04066711440682411, "decode.d3.loss_cls": 0.005642322232597507, "decode.d3.loss_mask": 0.01891419608145952, "decode.d3.loss_dice": 0.04083530604839325, "decode.d4.loss_cls": 0.005611454816244077, "decode.d4.loss_mask": 0.018657590076327325, "decode.d4.loss_dice": 0.04117581769824028, "time": 0.26025140285491943, "iter": 10000, "memory": 2807, "step": 10000} +{"aAcc": 98.96, "mIoU": 88.44, "mAcc": 92.73, "data_time": 0.002820611000061035, "time": 0.04976010322570801, "step": 10000} diff --git a/my_projects/conversion_tests/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/conv_cfg/maskformer_irl_vision_cat_hots_cat_irl_vision_cat.py b/my_projects/conversion_tests/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/conv_cfg/maskformer_irl_vision_cat_hots_cat_irl_vision_cat.py new file mode 100644 index 0000000000..c1db90fa17 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/conv_cfg/maskformer_irl_vision_cat_hots_cat_irl_vision_cat.py @@ -0,0 +1,526 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='HOTS_CAT', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS_CAT', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py b/my_projects/conversion_tests/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py new file mode 100644 index 0000000000..585dc791fb --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py @@ -0,0 +1,518 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240920_141808/vis_data/20240920_141808.json b/my_projects/conversion_tests/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240920_141808/vis_data/20240920_141808.json new file mode 100644 index 0000000000..b73514dfc9 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240920_141808/vis_data/20240920_141808.json @@ -0,0 +1,211 @@ +{"base_lr": 1.6011257771847904e-06, "lr": 1.6011257771847904e-06, "data_time": 0.003428483009338379, "loss": 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0000000000..98319fec51 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240920_141808/vis_data/config.py @@ -0,0 +1,321 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240920_141808/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240920_141808/vis_data/scalars.json new file mode 100644 index 0000000000..b73514dfc9 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240920_141808/vis_data/scalars.json @@ -0,0 +1,211 @@ +{"base_lr": 1.6011257771847904e-06, "lr": 1.6011257771847904e-06, "data_time": 0.003428483009338379, "loss": 3.0422013998031616, "decode.loss_ce": 3.0422013998031616, "decode.acc_seg": 8.04440975189209, "time": 0.10466759204864502, "iter": 41, "memory": 1871, "step": 41} +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.0036227941513061524, "loss": 2.973421025276184, "decode.loss_ce": 2.973421025276184, "decode.acc_seg": 6.809234619140625, 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"decode.acc_seg": 98.90995788574219, "time": 0.10228691101074219, "iter": 10000, "memory": 1092, "step": 10000} +{"aAcc": 98.53, "mIoU": 85.25, "mAcc": 91.51, "data_time": 0.0023306846618652345, "time": 0.020103764533996583, "step": 10000} diff --git a/my_projects/conversion_tests/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/conv_cfg/segformer_irl_vision_cat_hots_cat_irl_vision_cat.py b/my_projects/conversion_tests/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/conv_cfg/segformer_irl_vision_cat_hots_cat_irl_vision_cat.py new file mode 100644 index 0000000000..d1d285d77b --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/conv_cfg/segformer_irl_vision_cat_hots_cat_irl_vision_cat.py @@ -0,0 +1,329 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='HOTS_CAT', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS_CAT', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512.py b/my_projects/conversion_tests/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512.py new file mode 100644 index 0000000000..98319fec51 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512.py @@ -0,0 +1,321 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_143546/vis_data/20240920_143546.json b/my_projects/conversion_tests/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_143546/vis_data/20240920_143546.json new file mode 100644 index 0000000000..bd039afb70 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_143546/vis_data/20240920_143546.json @@ -0,0 +1,211 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100644 index 0000000000..be08b5a6df --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_143546/vis_data/config.py @@ -0,0 +1,343 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=26, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_143546/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240920_143546/vis_data/scalars.json new file mode 100644 index 0000000000..bd039afb70 --- /dev/null +++ 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a/my_projects/conversion_tests/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/conv_cfg/segnext_irl_vision_cat_hots_cat_irl_vision_cat.py b/my_projects/conversion_tests/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/conv_cfg/segnext_irl_vision_cat_hots_cat_irl_vision_cat.py new file mode 100644 index 0000000000..04877981ca --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/conv_cfg/segnext_irl_vision_cat_hots_cat_irl_vision_cat.py @@ -0,0 +1,351 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=26, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='HOTS_CAT', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS_CAT', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py b/my_projects/conversion_tests/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py new file mode 100644 index 0000000000..be08b5a6df --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py @@ -0,0 +1,343 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=26, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/20240805_024317/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/20240805_024317/vis_data/scalars.json new file mode 100644 index 0000000000..64d9fb8339 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/20240805_024317/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 1.97} +{"step": 2000, "mIoU": 19.79} +{"step": 3000, "mIoU": 40.13} +{"step": 4000, "mIoU": 51.99} +{"step": 5000, "mIoU": 63.36} +{"step": 6000, "mIoU": 68.4} +{"step": 7000, "mIoU": 68.55} +{"step": 8000, "mIoU": 70.28} +{"step": 9000, "mIoU": 72.84} +{"step": 10000, "mIoU": 72.8} diff --git a/my_projects/conversion_tests/selection_trained/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512.py b/my_projects/conversion_tests/selection_trained/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..8b4fa27721 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512.py @@ -0,0 +1,336 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/conv_cfg/bisenetv1_hots_cat_irl_vision_hots_cat.py b/my_projects/conversion_tests/selection_trained/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/conv_cfg/bisenetv1_hots_cat_irl_vision_hots_cat.py new file mode 100644 index 0000000000..f75893f071 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/conv_cfg/bisenetv1_hots_cat_irl_vision_hots_cat.py @@ -0,0 +1,343 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/conv_cfg/bisenetv1_irl_vision_cat_irl_vision_irl_vision_cat.py b/my_projects/conversion_tests/selection_trained/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/conv_cfg/bisenetv1_irl_vision_cat_irl_vision_irl_vision_cat.py new file mode 100644 index 0000000000..7e004230e8 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/conv_cfg/bisenetv1_irl_vision_cat_irl_vision_irl_vision_cat.py @@ -0,0 +1,343 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_042040/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_042040/vis_data/scalars.json new file mode 100644 index 0000000000..9d5cbec6e2 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_042040/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 47.08} +{"step": 2000, "mIoU": 73.71} +{"step": 3000, "mIoU": 86.34} +{"step": 4000, "mIoU": 85.83} +{"step": 5000, "mIoU": 83.01} +{"step": 6000, "mIoU": 84.03} +{"step": 7000, "mIoU": 85.76} +{"step": 8000, "mIoU": 88.94} +{"step": 9000, "mIoU": 90.91} +{"step": 10000, "mIoU": 88.91} diff --git a/my_projects/conversion_tests/selection_trained/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/conv_cfg/mask2former_hots_cat_irl_vision_hots_cat.py b/my_projects/conversion_tests/selection_trained/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/conv_cfg/mask2former_hots_cat_irl_vision_hots_cat.py new file mode 100644 index 0000000000..d5691ab930 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/conv_cfg/mask2former_hots_cat_irl_vision_hots_cat.py @@ -0,0 +1,482 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/conv_cfg/mask2former_irl_vision_cat_irl_vision_irl_vision_cat.py b/my_projects/conversion_tests/selection_trained/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/conv_cfg/mask2former_irl_vision_cat_irl_vision_irl_vision_cat.py new file mode 100644 index 0000000000..6340488dc2 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/conv_cfg/mask2former_irl_vision_cat_irl_vision_irl_vision_cat.py @@ -0,0 +1,482 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py b/my_projects/conversion_tests/selection_trained/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..103405e14f --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py @@ -0,0 +1,475 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_061403/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_061403/vis_data/scalars.json new file mode 100644 index 0000000000..3f3699e26d --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_061403/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 1.66} +{"step": 2000, "mIoU": 14.13} +{"step": 3000, "mIoU": 25.94} +{"step": 4000, "mIoU": 47.14} +{"step": 5000, "mIoU": 62.73} +{"step": 6000, "mIoU": 73.38} +{"step": 7000, "mIoU": 80.31} +{"step": 8000, "mIoU": 82.67} +{"step": 9000, "mIoU": 83.12} +{"step": 10000, "mIoU": 83.92} diff --git a/my_projects/conversion_tests/selection_trained/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/conv_cfg/maskformer_hots_cat_irl_vision_hots_cat.py b/my_projects/conversion_tests/selection_trained/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/conv_cfg/maskformer_hots_cat_irl_vision_hots_cat.py new file mode 100644 index 0000000000..a995f069b3 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/conv_cfg/maskformer_hots_cat_irl_vision_hots_cat.py @@ -0,0 +1,572 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/conv_cfg/maskformer_irl_vision_cat_irl_vision_irl_vision_cat.py b/my_projects/conversion_tests/selection_trained/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/conv_cfg/maskformer_irl_vision_cat_irl_vision_irl_vision_cat.py new file mode 100644 index 0000000000..67cf450b6f --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/conv_cfg/maskformer_irl_vision_cat_irl_vision_irl_vision_cat.py @@ -0,0 +1,572 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py b/my_projects/conversion_tests/selection_trained/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..f38140688b --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py @@ -0,0 +1,565 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/20240805_071527/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/20240805_071527/vis_data/scalars.json new file mode 100644 index 0000000000..ca7274c96e --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/20240805_071527/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 4.11} +{"step": 2000, "mIoU": 55.63} +{"step": 3000, "mIoU": 67.65} +{"step": 4000, "mIoU": 78.75} +{"step": 5000, "mIoU": 82.38} +{"step": 6000, "mIoU": 82.57} +{"step": 7000, "mIoU": 84.88} +{"step": 8000, "mIoU": 83.25} +{"step": 9000, "mIoU": 84.23} +{"step": 10000, "mIoU": 85.13} diff --git a/my_projects/conversion_tests/selection_trained/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/conv_cfg/segformer_hots_cat_irl_vision_hots_cat.py b/my_projects/conversion_tests/selection_trained/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/conv_cfg/segformer_hots_cat_irl_vision_hots_cat.py new file mode 100644 index 0000000000..a460557076 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/conv_cfg/segformer_hots_cat_irl_vision_hots_cat.py @@ -0,0 +1,329 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/conv_cfg/segformer_irl_vision_cat_irl_vision_irl_vision_cat.py b/my_projects/conversion_tests/selection_trained/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/conv_cfg/segformer_irl_vision_cat_irl_vision_irl_vision_cat.py new file mode 100644 index 0000000000..3e64d5978f --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/conv_cfg/segformer_irl_vision_cat_irl_vision_irl_vision_cat.py @@ -0,0 +1,329 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512.py b/my_projects/conversion_tests/selection_trained/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..7c56d446e5 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512.py @@ -0,0 +1,322 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_080116/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_080116/vis_data/scalars.json new file mode 100644 index 0000000000..c676752b87 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20240805_080116/vis_data/scalars.json @@ -0,0 +1,10 @@ +{"step": 1000, "mIoU": 5.71} +{"step": 2000, "mIoU": 50.8} +{"step": 3000, "mIoU": 70.42} +{"step": 4000, "mIoU": 80.92} +{"step": 5000, "mIoU": 82.79} +{"step": 6000, "mIoU": 85.41} +{"step": 7000, "mIoU": 85.65} +{"step": 8000, "mIoU": 87.74} +{"step": 9000, "mIoU": 87.87} +{"step": 10000, "mIoU": 88.97} diff --git a/my_projects/conversion_tests/selection_trained/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/conv_cfg/segnext_hots_cat_irl_vision_hots_cat.py b/my_projects/conversion_tests/selection_trained/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/conv_cfg/segnext_hots_cat_irl_vision_hots_cat.py new file mode 100644 index 0000000000..608676f311 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/conv_cfg/segnext_hots_cat_irl_vision_hots_cat.py @@ -0,0 +1,351 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=72, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/conv_cfg/segnext_irl_vision_cat_irl_vision_irl_vision_cat.py b/my_projects/conversion_tests/selection_trained/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/conv_cfg/segnext_irl_vision_cat_irl_vision_irl_vision_cat.py new file mode 100644 index 0000000000..23b1d3d6e0 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/conv_cfg/segnext_irl_vision_cat_irl_vision_irl_vision_cat.py @@ -0,0 +1,351 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=72, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py b/my_projects/conversion_tests/selection_trained/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..5d8bf45d9c --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py @@ -0,0 +1,344 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=72, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type="CustomIoUMetric") +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/20240924_110746.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/20240924_110746.json new file mode 100644 index 0000000000..1ed4f0a39f --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/20240924_110746.json @@ -0,0 +1,210 @@ +{"lr": 0.0006470980653769185, 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a/my_projects/conversion_tests/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/config.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/config.py new file mode 100644 index 0000000000..e3436abb10 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/config.py @@ -0,0 +1,336 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/scalars.json new file mode 100644 index 0000000000..1ed4f0a39f --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240924_110746/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"lr": 0.0006470980653769185, "data_time": 0.002445840835571289, "loss": 1.4245377123355865, "decode.loss_ce": 0.460920050740242, 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b/my_projects/conversion_tests/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..e3436abb10 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,336 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/conv_cfg/bisenetv1_hots_cat_irl_vision_cat_hots_cat.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/conv_cfg/bisenetv1_hots_cat_irl_vision_cat_hots_cat.py new file mode 100644 index 0000000000..b14755857c --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/conv_cfg/bisenetv1_hots_cat_irl_vision_cat_hots_cat.py @@ -0,0 +1,343 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_112623/vis_data/20240924_112623.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_112623/vis_data/20240924_112623.json new file mode 100644 index 0000000000..97a38b5081 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_112623/vis_data/20240924_112623.json @@ -0,0 +1,210 @@ +{"base_lr": 9.955884763429715e-05, "lr": 9.955884763429715e-06, "data_time": 0.00566253662109375, "grad_norm": 92.14458770751953, "loss": 12.631502532958985, "decode.loss_cls": 0.34186268597841263, "decode.loss_mask": 0.0973613753914833, "decode.loss_dice": 0.1479683645069599, "decode.d0.loss_cls": 6.51755952835083, "decode.d0.loss_mask": 0.14761312752962114, "decode.d0.loss_dice": 0.27674773782491685, "decode.d1.loss_cls": 0.415543670207262, "decode.d1.loss_mask": 0.10699977576732636, "decode.d1.loss_dice": 0.17495039775967597, "decode.d2.loss_cls": 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index 0000000000..d802256f03 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_112623/vis_data/config.py @@ -0,0 +1,433 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_112623/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_112623/vis_data/scalars.json new file mode 100644 index 0000000000..97a38b5081 --- /dev/null +++ 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0.0017017439276969526, "decode.d8.loss_mask": 0.05127977039664984, "decode.d8.loss_dice": 0.11738242879509926, "time": 0.4015626907348633, "iter": 10000, "memory": 2862, "step": 10000} +{"aAcc": 99.73, "mIoU": 87.5, "mAcc": 92.16, "data_time": 0.0017285744349161785, "time": 0.07100030581156412, "step": 10000} diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/conv_cfg/mask2former_hots_cat_irl_vision_cat_hots_cat.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/conv_cfg/mask2former_hots_cat_irl_vision_cat_hots_cat.py new file mode 100644 index 0000000000..1ec2ae29b5 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/conv_cfg/mask2former_hots_cat_irl_vision_cat_hots_cat.py @@ -0,0 +1,440 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..d802256f03 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,433 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_124010/vis_data/20240924_124010.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_124010/vis_data/20240924_124010.json new file mode 100644 index 0000000000..00e6bb52ec --- /dev/null +++ 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a/my_projects/conversion_tests/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_124010/vis_data/config.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_124010/vis_data/config.py new file mode 100644 index 0000000000..28082765fa --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_124010/vis_data/config.py @@ -0,0 +1,523 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_124010/vis_data/scalars.json 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0.0016722360589483288, "decode.d4.loss_mask": 0.00552055579610169, "decode.d4.loss_dice": 0.013965561613440513, "time": 0.2539757966995239, "iter": 10000, "memory": 2429, "step": 10000} +{"aAcc": 99.71, "mIoU": 89.28, "mAcc": 94.38, "data_time": 0.0016840457916259765, "time": 0.047839935620625815, "step": 10000} diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/conv_cfg/maskformer_hots_cat_irl_vision_cat_hots_cat.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/conv_cfg/maskformer_hots_cat_irl_vision_cat_hots_cat.py new file mode 100644 index 0000000000..4c26c2fd10 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/conv_cfg/maskformer_hots_cat_irl_vision_cat_hots_cat.py @@ -0,0 +1,530 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..28082765fa --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,523 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/20240924_132330.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/20240924_132330.json new file mode 100644 index 0000000000..f601f3a3c6 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/20240924_132330.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.003247380256652832, "loss": 3.061506676673889, "decode.loss_ce": 3.061506676673889, "decode.acc_seg": 14.821052551269531, "time": 0.0987546682357788, 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"decode.loss_ce": 0.00769683497492224, "decode.acc_seg": 99.67753601074219, "time": 0.09499402046203613, "iter": 10000, "memory": 1092, "step": 10000} +{"aAcc": 99.62, "mIoU": 87.51, "mAcc": 94.92, "data_time": 0.0015500783920288086, "time": 0.01838030815124512, "step": 10000} diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/config.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/config.py new file mode 100644 index 0000000000..9268b6dc88 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/config.py @@ -0,0 +1,322 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/scalars.json new file mode 100644 index 0000000000..f601f3a3c6 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240924_132330/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.003247380256652832, "loss": 3.061506676673889, "decode.loss_ce": 3.061506676673889, "decode.acc_seg": 14.821052551269531, "time": 0.0987546682357788, "iter": 50, "memory": 2002, "step": 50} +{"base_lr": 3.962697798532355e-06, "lr": 3.962697798532355e-06, "data_time": 0.003378415107727051, "loss": 2.655226707458496, "decode.loss_ce": 2.655226707458496, "decode.acc_seg": 91.31123352050781, "time": 0.09768564701080322, "iter": 100, "memory": 1092, "step": 100} +{"base_lr": 5.964030020013346e-06, "lr": 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a/my_projects/conversion_tests/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/conv_cfg/segformer_hots_cat_irl_vision_cat_hots_cat.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/conv_cfg/segformer_hots_cat_irl_vision_cat_hots_cat.py new file mode 100644 index 0000000000..b4232216c5 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/conv_cfg/segformer_hots_cat_irl_vision_cat_hots_cat.py @@ -0,0 +1,329 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..9268b6dc88 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,322 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_133957/vis_data/20240924_133957.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_133957/vis_data/20240924_133957.json new file mode 100644 index 0000000000..7dea46ef02 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_133957/vis_data/20240924_133957.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 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0.0, "data_time": 0.00474236011505127, "loss": 0.008355554286390543, "decode.loss_ce": 0.008355554286390543, "decode.acc_seg": 99.82452392578125, "time": 0.14334065914154054, "iter": 10000, "memory": 1334, "step": 10000} +{"aAcc": 99.72, "mIoU": 89.91, "mAcc": 94.83, "data_time": 0.0015585581461588542, "time": 0.0221388578414917, "step": 10000} diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_133957/vis_data/config.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_133957/vis_data/config.py new file mode 100644 index 0000000000..b52e5d405d --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_133957/vis_data/config.py @@ -0,0 +1,344 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=30, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_133957/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_133957/vis_data/scalars.json new file mode 100644 index 0000000000..7dea46ef02 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240924_133957/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.004604220390319824, "loss": 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"step": 9600} +{"base_lr": 2.470878926932549e-06, "lr": 2.470878926932549e-06, "data_time": 0.004555702209472656, "loss": 0.00918987044133246, "decode.loss_ce": 0.00918987044133246, "decode.acc_seg": 99.77569580078125, "time": 0.1422152042388916, "iter": 9650, "memory": 1335, "step": 9650} +{"base_lr": 2.117896223085038e-06, "lr": 2.117896223085038e-06, "data_time": 0.004589748382568359, "loss": 0.009242817061021924, "decode.loss_ce": 0.009242817061021924, "decode.acc_seg": 99.7812271118164, "time": 0.14260950088500976, "iter": 9700, "memory": 1335, "step": 9700} +{"base_lr": 1.7649135192375295e-06, "lr": 1.7649135192375295e-06, "data_time": 0.004685449600219727, "loss": 0.018916840851306915, "decode.loss_ce": 0.018916840851306915, "decode.acc_seg": 99.7542495727539, "time": 0.1416496753692627, "iter": 9750, "memory": 1334, "step": 9750} +{"base_lr": 1.411930815390025e-06, "lr": 1.411930815390025e-06, "data_time": 0.004578423500061035, "loss": 0.008022022107616067, "decode.loss_ce": 0.008022022107616067, "decode.acc_seg": 99.70647430419922, "time": 0.1425941228866577, "iter": 9800, "memory": 1334, "step": 9800} +{"base_lr": 1.0589481115425196e-06, "lr": 1.0589481115425196e-06, "data_time": 0.004559040069580078, "loss": 0.007417794410139322, "decode.loss_ce": 0.007417794410139322, "decode.acc_seg": 99.79361724853516, "time": 0.14209330081939697, "iter": 9850, "memory": 1335, "step": 9850} +{"base_lr": 7.059654076950137e-07, "lr": 7.059654076950137e-07, "data_time": 0.004599928855895996, "loss": 0.008108102879486979, "decode.loss_ce": 0.008108102879486979, "decode.acc_seg": 99.75032806396484, "time": 0.14212267398834227, "iter": 9900, "memory": 1334, "step": 9900} +{"base_lr": 3.529827038475068e-07, "lr": 3.529827038475068e-07, "data_time": 0.004612278938293457, "loss": 0.007870565354824065, "decode.loss_ce": 0.007870565354824065, "decode.acc_seg": 99.78981018066406, "time": 0.14250736236572265, "iter": 9950, "memory": 1335, "step": 9950} +{"base_lr": 0.0, "lr": 0.0, "data_time": 0.00474236011505127, "loss": 0.008355554286390543, "decode.loss_ce": 0.008355554286390543, "decode.acc_seg": 99.82452392578125, "time": 0.14334065914154054, "iter": 10000, "memory": 1334, "step": 10000} +{"aAcc": 99.72, "mIoU": 89.91, "mAcc": 94.83, "data_time": 0.0015585581461588542, "time": 0.0221388578414917, "step": 10000} diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/conv_cfg/segnext_hots_cat_irl_vision_cat_hots_cat.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/conv_cfg/segnext_hots_cat_irl_vision_cat_hots_cat.py new file mode 100644 index 0000000000..03e09987a4 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/conv_cfg/segnext_hots_cat_irl_vision_cat_hots_cat.py @@ -0,0 +1,351 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=30, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..b52e5d405d --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,344 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=30, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512/20241010_141117/vis_data/20241010_141117.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512/20241010_141117/vis_data/20241010_141117.json new file mode 100644 index 0000000000..11a98ab02e --- /dev/null +++ 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47.99, "data_time": 0.001800076166788737, "time": 0.014354594548543294, "step": 2000} diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512/20241010_141117/vis_data/config.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512/20241010_141117/vis_data/config.py new file mode 100644 index 0000000000..a224d80819 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512/20241010_141117/vis_data/config.py @@ -0,0 +1,336 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=2000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512/20241010_141117/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512/20241010_141117/vis_data/scalars.json new file mode 100644 index 0000000000..11a98ab02e --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512/20241010_141117/vis_data/scalars.json @@ -0,0 +1,42 @@ +{"lr": 0.0006470980653769185, "data_time": 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a/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..a224d80819 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,336 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=2000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512/conv_cfg/bisenetv1_hots_cat_irl_vision_cat_hots_cat.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512/conv_cfg/bisenetv1_hots_cat_irl_vision_cat_hots_cat.py new file mode 100644 index 0000000000..a712c23b31 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512/conv_cfg/bisenetv1_hots_cat_irl_vision_cat_hots_cat.py @@ -0,0 +1,343 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=2000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-2k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/mask2former_r50_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/20241010_141458/vis_data/config.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/mask2former_r50_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/20241010_141458/vis_data/config.py new file mode 100644 index 0000000000..ae93d83c1e --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/mask2former_r50_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/20241010_141458/vis_data/config.py @@ -0,0 +1,433 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=2000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/mask2former_r50_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/conv_cfg/mask2former_hots_cat_irl_vision_cat_hots_cat.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/mask2former_r50_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/conv_cfg/mask2former_hots_cat_irl_vision_cat_hots_cat.py new file mode 100644 index 0000000000..1bd3e55130 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/mask2former_r50_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/conv_cfg/mask2former_hots_cat_irl_vision_cat_hots_cat.py @@ -0,0 +1,440 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=2000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/mask2former_r50_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/mask2former_r50_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/mask2former_r50_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/mask2former_r50_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..ae93d83c1e --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/mask2former_r50_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/mask2former_r50_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,433 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=2000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/20241010_141511/vis_data/20241010_141511.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/20241010_141511/vis_data/20241010_141511.json new file mode 100644 index 0000000000..48d9618f65 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/20241010_141511/vis_data/20241010_141511.json @@ -0,0 +1,42 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 0.005037999153137207, "grad_norm": 603.4740905761719, "loss": 19.74930820465088, "decode.loss_cls": 2.6145468235015867, "decode.loss_mask": 0.19152947813272475, "decode.loss_dice": 0.16488550528883933, "decode.d0.loss_cls": 3.158870053291321, "decode.d0.loss_mask": 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b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/20241010_141511/vis_data/config.py new file mode 100644 index 0000000000..91b76d8ed9 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/20241010_141511/vis_data/config.py @@ -0,0 +1,523 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=2000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/20241010_141511/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/20241010_141511/vis_data/scalars.json new file mode 100644 index 0000000000..48d9618f65 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/20241010_141511/vis_data/scalars.json @@ -0,0 +1,42 @@ +{"base_lr": 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a/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/conv_cfg/maskformer_hots_cat_irl_vision_cat_hots_cat.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/conv_cfg/maskformer_hots_cat_irl_vision_cat_hots_cat.py new file mode 100644 index 0000000000..fbb275903f --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/conv_cfg/maskformer_hots_cat_irl_vision_cat_hots_cat.py @@ -0,0 +1,530 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=2000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..91b76d8ed9 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,523 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=2000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512/20241010_142409/vis_data/20241010_142409.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512/20241010_142409/vis_data/20241010_142409.json new file mode 100644 index 0000000000..4ca2e86fe0 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512/20241010_142409/vis_data/20241010_142409.json @@ -0,0 +1,42 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.003375244140625, "loss": 3.059952235221863, "decode.loss_ce": 3.059952235221863, "decode.acc_seg": 17.77075958251953, "time": 0.09767737388610839, "iter": 50, "memory": 2002, "step": 50} +{"base_lr": 3.962697798532355e-06, "lr": 3.962697798532355e-06, "data_time": 0.003383970260620117, "loss": 2.8471357345581056, "decode.loss_ce": 2.8471357345581056, "decode.acc_seg": 85.91499328613281, "time": 0.09715592861175537, "iter": 100, "memory": 1028, "step": 100} +{"base_lr": 5.964030020013346e-06, "lr": 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b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512/20241010_142409/vis_data/config.py new file mode 100644 index 0000000000..83b2d0fcff --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512/20241010_142409/vis_data/config.py @@ -0,0 +1,322 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=2000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512/20241010_142409/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512/20241010_142409/vis_data/scalars.json new file mode 100644 index 0000000000..4ca2e86fe0 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512/20241010_142409/vis_data/scalars.json @@ -0,0 +1,42 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.003375244140625, "loss": 3.059952235221863, "decode.loss_ce": 3.059952235221863, "decode.acc_seg": 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"time": 0.09513134956359863, "iter": 2000, "memory": 1028, "step": 2000} +{"aAcc": 99.05, "mIoU": 62.64, "mAcc": 71.47, "data_time": 0.001618822415669759, "time": 0.018232480684916178, "step": 2000} diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512/conv_cfg/segformer_hots_cat_irl_vision_cat_hots_cat.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512/conv_cfg/segformer_hots_cat_irl_vision_cat_hots_cat.py new file mode 100644 index 0000000000..a60a2dc18e --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512/conv_cfg/segformer_hots_cat_irl_vision_cat_hots_cat.py @@ -0,0 +1,329 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=2000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..83b2d0fcff --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,322 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=2000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-2k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/20241010_142737/vis_data/20241010_142737.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/20241010_142737/vis_data/20241010_142737.json new file mode 100644 index 0000000000..8a05eaaba8 --- /dev/null +++ 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b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/20241010_142737/vis_data/config.py @@ -0,0 +1,344 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=30, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=2000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/20241010_142737/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/20241010_142737/vis_data/scalars.json new file mode 100644 index 0000000000..8a05eaaba8 --- /dev/null +++ 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1750, "memory": 1332, "step": 1750} +{"base_lr": 5.788916343099198e-05, "lr": 5.788916343099198e-05, "data_time": 0.004563450813293457, "loss": 0.056241477653384206, "decode.loss_ce": 0.056241477653384206, "decode.acc_seg": 98.30084991455078, "time": 0.1450204372406006, "iter": 1800, "memory": 1333, "step": 1800} +{"base_lr": 5.7536180727144505e-05, "lr": 5.7536180727144505e-05, "data_time": 0.00484771728515625, "loss": 0.05463843550533056, "decode.loss_ce": 0.05463843550533056, "decode.acc_seg": 98.83456420898438, "time": 0.15503861904144287, "iter": 1850, "memory": 1333, "step": 1850} +{"base_lr": 5.718319802329701e-05, "lr": 5.718319802329701e-05, "data_time": 0.004890799522399902, "loss": 0.046077203936874866, "decode.loss_ce": 0.046077203936874866, "decode.acc_seg": 99.51411437988281, "time": 0.15876309871673583, "iter": 1900, "memory": 1333, "step": 1900} +{"base_lr": 5.683021531944951e-05, "lr": 5.683021531944951e-05, "data_time": 0.0047025918960571286, "loss": 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0000000000..f50fe52ce2 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_2k/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-2k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,344 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=30, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=2000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20241015_093911/vis_data/20241015_093911.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20241015_093911/vis_data/20241015_093911.json new file mode 100644 index 0000000000..32cde5bebf --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20241015_093911/vis_data/20241015_093911.json @@ -0,0 +1,210 @@ +{"lr": 0.0006470980653769185, "data_time": 0.002411675453186035, "loss": 0.8630707263946533, "decode.loss_ce": 0.27609404772520063, "decode.acc_seg": 94.0786361694336, "aux_0.loss_ce": 0.29503906071186065, "aux_0.acc_seg": 94.0786361694336, "aux_1.loss_ce": 0.29193760752677916, "aux_1.acc_seg": 94.0786361694336, "time": 0.09332466125488281, "iter": 50, "memory": 2548, "step": 50} +{"lr": 0.0007971981320880594, "data_time": 0.0024655818939208984, "loss": 1.358642864227295, "decode.loss_ce": 0.42481433749198916, "decode.acc_seg": 80.24205017089844, "aux_0.loss_ce": 0.45490098297595977, "aux_0.acc_seg": 80.24205017089844, "aux_1.loss_ce": 0.4789275243878365, "aux_1.acc_seg": 80.24205017089844, "time": 0.09388020038604736, "iter": 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0.00163880189259847, "time": 0.013609766960144043, "step": 10000} diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20241015_093911/vis_data/config.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20241015_093911/vis_data/config.py new file mode 100644 index 0000000000..82d69eb6c6 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20241015_093911/vis_data/config.py @@ -0,0 +1,337 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20241015_093911/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20241015_093911/vis_data/scalars.json new file mode 100644 index 0000000000..32cde5bebf --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20241015_093911/vis_data/scalars.json @@ -0,0 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a/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..82d69eb6c6 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,337 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/conv_cfg/bisenetv1_hots_cat_irl_vision_cat_hots_cat.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/conv_cfg/bisenetv1_hots_cat_irl_vision_cat_hots_cat.py new file mode 100644 index 0000000000..106793af62 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/conv_cfg/bisenetv1_hots_cat_irl_vision_cat_hots_cat.py @@ -0,0 +1,344 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_095615/vis_data/20241015_095615.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_095615/vis_data/20241015_095615.json new file mode 100644 index 0000000000..8172b0db92 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_095615/vis_data/20241015_095615.json @@ -0,0 +1,210 @@ +{"base_lr": 9.955884763429715e-05, "lr": 9.955884763429715e-06, "data_time": 0.00528111457824707, "grad_norm": 111.47394332885742, "loss": 14.033984375, "decode.loss_cls": 0.43480961620807645, "decode.loss_mask": 0.13393065556883813, "decode.loss_dice": 0.1881912514567375, 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a/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_095615/vis_data/config.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_095615/vis_data/config.py new file mode 100644 index 0000000000..2d50ecbeec --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_095615/vis_data/config.py @@ -0,0 +1,434 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_095615/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_095615/vis_data/scalars.json new file mode 100644 index 0000000000..8172b0db92 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_095615/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 9.955884763429715e-05, "lr": 9.955884763429715e-06, "data_time": 0.00528111457824707, "grad_norm": 111.47394332885742, "loss": 14.033984375, "decode.loss_cls": 0.43480961620807645, "decode.loss_mask": 0.13393065556883813, "decode.loss_dice": 0.1881912514567375, "decode.d0.loss_cls": 6.660710382461548, "decode.d0.loss_mask": 0.13922085762023925, "decode.d0.loss_dice": 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b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/conv_cfg/mask2former_hots_cat_irl_vision_cat_hots_cat.py new file mode 100644 index 0000000000..d8a1ad0c59 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/conv_cfg/mask2former_hots_cat_irl_vision_cat_hots_cat.py @@ -0,0 +1,441 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..2d50ecbeec --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,434 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_110407/vis_data/20241015_110407.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_110407/vis_data/20241015_110407.json new file mode 100644 index 0000000000..2dbc22fdf3 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_110407/vis_data/20241015_110407.json @@ -0,0 +1,210 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 0.004987859725952148, "grad_norm": 621.3393798828125, "loss": 22.60122127532959, "decode.loss_cls": 3.2704917192459106, "decode.loss_mask": 0.14756483882665633, "decode.loss_dice": 0.17138329669833183, "decode.d0.loss_cls": 3.5796732902526855, 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dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_110407/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_110407/vis_data/scalars.json new file mode 100644 index 0000000000..2dbc22fdf3 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241015_110407/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 0.004987859725952148, "grad_norm": 621.3393798828125, "loss": 22.60122127532959, "decode.loss_cls": 3.2704917192459106, "decode.loss_mask": 0.14756483882665633, "decode.loss_dice": 0.17138329669833183, "decode.d0.loss_cls": 3.5796732902526855, "decode.d0.loss_mask": 0.1255863331258297, 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"decode.d4.loss_mask": 0.010207669204100966, "decode.d4.loss_dice": 0.019936266541481017, "time": 0.25200376510620115, "iter": 9950, "memory": 2429, "step": 9950} +{"base_lr": 0.0, "lr": 0.0, "data_time": 0.005083942413330078, "grad_norm": 4.320200711488724, "loss": 0.27931518107652664, "decode.loss_cls": 0.0077530091370135775, "decode.loss_mask": 0.007839737832546234, "decode.loss_dice": 0.029057373758405447, "decode.d0.loss_cls": 0.0126910609746119, "decode.d0.loss_mask": 0.009428284596651793, "decode.d0.loss_dice": 0.030798789393156768, "decode.d1.loss_cls": 0.007532276440178976, "decode.d1.loss_mask": 0.008355768164619803, "decode.d1.loss_dice": 0.03059236314147711, "decode.d2.loss_cls": 0.008669024708797223, "decode.d2.loss_mask": 0.008117251843214036, "decode.d2.loss_dice": 0.029108216520398855, "decode.d3.loss_cls": 0.00819929042700096, "decode.d3.loss_mask": 0.007855188194662332, "decode.d3.loss_dice": 0.028710821457207204, "decode.d4.loss_cls": 0.007639522684621624, "decode.d4.loss_mask": 0.007871839171275496, "decode.d4.loss_dice": 0.029095346760004758, "time": 0.25204076766967776, "iter": 10000, "memory": 2466, "step": 10000} +{"aAcc": 99.69, "mIoU": 89.23, "mAcc": 94.35, "data_time": 0.0016114155451456705, "time": 0.047709202766418456, "step": 10000} diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/conv_cfg/maskformer_hots_cat_irl_vision_cat_hots_cat.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/conv_cfg/maskformer_hots_cat_irl_vision_cat_hots_cat.py new file mode 100644 index 0000000000..9f7027e6a3 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/conv_cfg/maskformer_hots_cat_irl_vision_cat_hots_cat.py @@ -0,0 +1,531 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..6c7cf167da --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,524 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20241015_114713/vis_data/20241015_114713.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20241015_114713/vis_data/20241015_114713.json new file mode 100644 index 0000000000..332657c969 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20241015_114713/vis_data/20241015_114713.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.003410649299621582, "loss": 3.111463022232056, "decode.loss_ce": 3.111463022232056, "decode.acc_seg": 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+checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20241015_114713/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20241015_114713/vis_data/scalars.json new file mode 100644 index 0000000000..332657c969 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20241015_114713/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.003410649299621582, "loss": 3.111463022232056, "decode.loss_ce": 3.111463022232056, "decode.acc_seg": 20.90740203857422, "time": 0.09794931411743164, "iter": 50, "memory": 2002, "step": 50} +{"base_lr": 3.962697798532355e-06, "lr": 3.962697798532355e-06, "data_time": 0.0032634973526000977, "loss": 2.607606840133667, "decode.loss_ce": 2.607606840133667, 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10000} +{"aAcc": 99.6, "mIoU": 86.9, "mAcc": 94.38, "data_time": 0.0015838146209716797, "time": 0.018149908383687338, "step": 10000} diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/conv_cfg/segformer_hots_cat_irl_vision_cat_hots_cat.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/conv_cfg/segformer_hots_cat_irl_vision_cat_hots_cat.py new file mode 100644 index 0000000000..038cac7bc1 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/conv_cfg/segformer_hots_cat_irl_vision_cat_hots_cat.py @@ -0,0 +1,330 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..2b5872074c --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,323 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/20241015_120332/vis_data/20241015_120332.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/20241015_120332/vis_data/20241015_120332.json new file mode 100644 index 0000000000..edbb9e7053 --- /dev/null +++ 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"data_time": 0.004631495475769043, "loss": 0.011922908062115312, "decode.loss_ce": 0.011922908062115312, "decode.acc_seg": 99.71866607666016, "time": 0.14250097274780274, "iter": 9950, "memory": 1332, "step": 9950} +{"base_lr": 0.0, "lr": 0.0, "data_time": 0.004599809646606445, "loss": 0.009129674686118961, "decode.loss_ce": 0.009129674686118961, "decode.acc_seg": 99.69921112060547, "time": 0.1427034854888916, "iter": 10000, "memory": 1332, "step": 10000} +{"aAcc": 99.7, "mIoU": 90.06, "mAcc": 94.76, "data_time": 0.0017589886983235678, "time": 0.02174348831176758, "step": 10000} diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/20241015_120332/vis_data/config.py b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/20241015_120332/vis_data/config.py new file mode 100644 index 0000000000..ac75c7a729 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/20241015_120332/vis_data/config.py @@ -0,0 +1,345 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=30, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/20241015_120332/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/20241015_120332/vis_data/scalars.json new file mode 100644 index 0000000000..edbb9e7053 --- /dev/null +++ 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mode 100644 index 0000000000..ac75c7a729 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/irl_vision_cat_pmd/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,345 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=30, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-5k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/selection_trained/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/20241027_191703/vis_data/20241027_191703.json b/my_projects/conversion_tests/selection_trained/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/20241027_191703/vis_data/20241027_191703.json new file mode 100644 index 0000000000..df14178012 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/20241027_191703/vis_data/20241027_191703.json @@ -0,0 +1,210 @@ +{"lr": 0.0006470980653769185, "data_time": 0.0025153160095214844, "loss": 1.3102005422115326, "decode.loss_ce": 0.42309882640838625, "decode.acc_seg": 93.8547134399414, "aux_0.loss_ce": 0.450121234357357, "aux_0.acc_seg": 93.8547134399414, "aux_1.loss_ce": 0.43698047399520873, "aux_1.acc_seg": 93.8547134399414, "time": 0.0948939323425293, "iter": 50, "memory": 2548, "step": 50} +{"lr": 0.0007971981320880594, "data_time": 0.002527189254760742, "loss": 1.5039239048957824, "decode.loss_ce": 0.48357090950012205, "decode.acc_seg": 92.5853500366211, "aux_0.loss_ce": 0.5036907523870469, "aux_0.acc_seg": 92.61505889892578, "aux_1.loss_ce": 0.5166622310876846, "aux_1.acc_seg": 92.83466339111328, "time": 0.09583389759063721, "iter": 100, "memory": 823, "step": 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10000} diff --git a/my_projects/conversion_tests/selection_trained/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/20241027_191703/vis_data/config.py b/my_projects/conversion_tests/selection_trained/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/20241027_191703/vis_data/config.py new file mode 100644 index 0000000000..b9fea1c24c --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/20241027_191703/vis_data/config.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512' diff --git a/my_projects/conversion_tests/selection_trained/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/20241027_191703/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/20241027_191703/vis_data/scalars.json new file mode 100644 index 0000000000..df14178012 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/20241027_191703/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"lr": 0.0006470980653769185, "data_time": 0.0025153160095214844, "loss": 1.3102005422115326, "decode.loss_ce": 0.42309882640838625, "decode.acc_seg": 93.8547134399414, "aux_0.loss_ce": 0.450121234357357, "aux_0.acc_seg": 93.8547134399414, "aux_1.loss_ce": 0.43698047399520873, "aux_1.acc_seg": 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b/my_projects/conversion_tests/selection_trained/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512.py new file mode 100644 index 0000000000..b9fea1c24c --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512' diff --git a/my_projects/conversion_tests/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_193429/vis_data/20241027_193429.json b/my_projects/conversion_tests/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_193429/vis_data/20241027_193429.json new file mode 100644 index 0000000000..43eaf33010 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_193429/vis_data/20241027_193429.json @@ -0,0 +1,210 @@ +{"base_lr": 9.955884763429715e-05, "lr": 9.955884763429715e-06, "data_time": 0.0051276922225952145, "grad_norm": 177.94206733703612, "loss": 18.886375427246094, "decode.loss_cls": 0.48950027823448183, "decode.loss_mask": 0.22267264798283576, "decode.loss_dice": 0.522909551858902, "decode.d0.loss_cls": 6.529542350769043, "decode.d0.loss_mask": 0.2583734855055809, "decode.d0.loss_dice": 0.6232244476675988, "decode.d1.loss_cls": 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b/my_projects/conversion_tests/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_193429/vis_data/config.py new file mode 100644 index 0000000000..978405a37e --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_193429/vis_data/config.py @@ -0,0 +1,431 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=31, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 31 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512' diff --git a/my_projects/conversion_tests/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_193429/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_193429/vis_data/scalars.json new file mode 100644 index 0000000000..43eaf33010 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_193429/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 9.955884763429715e-05, "lr": 9.955884763429715e-06, "data_time": 0.0051276922225952145, "grad_norm": 177.94206733703612, "loss": 18.886375427246094, "decode.loss_cls": 0.48950027823448183, "decode.loss_mask": 0.22267264798283576, "decode.loss_dice": 0.522909551858902, "decode.d0.loss_cls": 6.529542350769043, "decode.d0.loss_mask": 0.2583734855055809, "decode.d0.loss_dice": 0.6232244476675988, "decode.d1.loss_cls": 0.5642811685800553, "decode.d1.loss_mask": 0.19858563058078288, "decode.d1.loss_dice": 0.53911302536726, "decode.d2.loss_cls": 0.5574976116418838, "decode.d2.loss_mask": 0.256547249853611, "decode.d2.loss_dice": 0.5689449168741703, "decode.d3.loss_cls": 0.533391535282135, "decode.d3.loss_mask": 0.22511174753308297, "decode.d3.loss_dice": 0.5350216031074524, "decode.d4.loss_cls": 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0.16621680036187172, "decode.d8.loss_cls": 0.013443533501413186, "decode.d8.loss_mask": 0.09279430918395519, "decode.d8.loss_dice": 0.16148251444101333, "time": 0.4018991470336914, "iter": 10000, "memory": 2911, "step": 10000} +{"aAcc": 99.56, "mIoU": 91.36, "mAcc": 95.14, "data_time": 0.0016731054354936648, "time": 0.07098609973222782, "step": 10000} diff --git a/my_projects/conversion_tests/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512.py b/my_projects/conversion_tests/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512.py new file mode 100644 index 0000000000..978405a37e --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512.py @@ -0,0 +1,431 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/mask2former/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512_20221204_000055-2d1f55f1.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=31, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 31 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512' diff --git a/my_projects/conversion_tests/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_204248/vis_data/20241027_204248.json b/my_projects/conversion_tests/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_204248/vis_data/20241027_204248.json new file mode 100644 index 0000000000..30bb917303 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_204248/vis_data/20241027_204248.json @@ -0,0 +1,210 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 0.004942417144775391, "grad_norm": 551.2614044189453, "loss": 24.15789852142334, "decode.loss_cls": 3.701165723800659, "decode.loss_mask": 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b/my_projects/conversion_tests/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_204248/vis_data/config.py new file mode 100644 index 0000000000..534c342793 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_204248/vis_data/config.py @@ -0,0 +1,521 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=31, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 31 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512' diff --git a/my_projects/conversion_tests/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_204248/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_204248/vis_data/scalars.json new file mode 100644 index 0000000000..30bb917303 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_204248/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 5.881770354070807e-07, "lr": 5.881770354070807e-07, "data_time": 0.004942417144775391, "grad_norm": 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b/my_projects/conversion_tests/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512.py new file mode 100644 index 0000000000..534c342793 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512.py @@ -0,0 +1,521 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512_20221114_232813-f14e7ce0.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=31, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 31 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512' diff --git a/my_projects/conversion_tests/selection_trained/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/20241027_212628/vis_data/20241027_212628.json b/my_projects/conversion_tests/selection_trained/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/20241027_212628/vis_data/20241027_212628.json new file mode 100644 index 0000000000..8af950fb60 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/20241027_212628/vis_data/20241027_212628.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.0033128023147583007, 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+crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_SODHOTS-C-512x512' diff --git a/my_projects/conversion_tests/selection_trained/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/20241027_212628/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/20241027_212628/vis_data/scalars.json new file mode 100644 index 0000000000..8af950fb60 --- /dev/null +++ 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"data_time": 0.003245973587036133, "loss": 0.00838554953224957, "decode.loss_ce": 0.00838554953224957, "decode.acc_seg": 99.635986328125, "time": 0.09634754657745362, "iter": 9950, "memory": 1032, "step": 9950} +{"base_lr": 0.0, "lr": 0.0, "data_time": 0.003344821929931641, "loss": 0.016442144964821638, "decode.loss_ce": 0.016442144964821638, "decode.acc_seg": 99.37744140625, "time": 0.09631509780883789, "iter": 10000, "memory": 1032, "step": 10000} +{"aAcc": 99.2, "mIoU": 84.98, "mAcc": 92.03, "data_time": 0.001484088408641326, "time": 0.018106546157445662, "step": 10000} diff --git a/my_projects/conversion_tests/selection_trained/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/segformer_mit-b0_1xb2-10k_sodhots-c-512x512.py b/my_projects/conversion_tests/selection_trained/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/segformer_mit-b0_1xb2-10k_sodhots-c-512x512.py new file mode 100644 index 0000000000..ae2fafcc93 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/segformer_mit-b0_1xb2-10k_sodhots-c-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segformer_mit-b0_1xb2-10k_sodhots-c-512x512' diff --git a/my_projects/conversion_tests/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_214304/vis_data/20241027_214304.json b/my_projects/conversion_tests/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_214304/vis_data/20241027_214304.json new file mode 100644 index 0000000000..9927998624 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_214304/vis_data/20241027_214304.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.004518485069274903, "loss": 2.879839825630188, "decode.loss_ce": 2.879839825630188, "decode.acc_seg": 61.93370819091797, "time": 0.14556424617767333, "iter": 50, "memory": 3233, "step": 50} +{"base_lr": 3.962697798532355e-06, "lr": 3.962697798532355e-06, "data_time": 0.00457608699798584, "loss": 2.5395385384559632, "decode.loss_ce": 2.5395385384559632, 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+{"base_lr": 7.059654076950137e-07, "lr": 7.059654076950137e-07, "data_time": 0.0047095298767089845, "loss": 0.02538012508302927, "decode.loss_ce": 0.02538012508302927, "decode.acc_seg": 98.64635467529297, "time": 0.14921469688415528, "iter": 9900, "memory": 1339, "step": 9900} +{"base_lr": 3.529827038475068e-07, "lr": 3.529827038475068e-07, "data_time": 0.004756045341491699, "loss": 0.012506967689841986, "decode.loss_ce": 0.012506967689841986, "decode.acc_seg": 99.81632232666016, "time": 0.14499831199645996, "iter": 9950, "memory": 1338, "step": 9950} +{"base_lr": 0.0, "lr": 0.0, "data_time": 0.004836726188659668, "loss": 0.020111867878586053, "decode.loss_ce": 0.020111867878586053, "decode.acc_seg": 99.25785064697266, "time": 0.14681808948516845, "iter": 10000, "memory": 1338, "step": 10000} +{"aAcc": 99.43, "mIoU": 87.38, "mAcc": 92.79, "data_time": 0.001533991251236353, "time": 0.0221102115435478, "step": 10000} diff --git a/my_projects/conversion_tests/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_214304/vis_data/config.py b/my_projects/conversion_tests/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_214304/vis_data/config.py new file mode 100644 index 0000000000..9cc09b4bde --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_214304/vis_data/config.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=31, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-5k_SODHOTS-C-512x512' diff --git a/my_projects/conversion_tests/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_214304/vis_data/scalars.json b/my_projects/conversion_tests/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_214304/vis_data/scalars.json new file mode 100644 index 0000000000..9927998624 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_214304/vis_data/scalars.json @@ -0,0 +1,210 @@ +{"base_lr": 1.9613655770513682e-06, "lr": 1.9613655770513682e-06, "data_time": 0.004518485069274903, "loss": 2.879839825630188, "decode.loss_ce": 2.879839825630188, "decode.acc_seg": 61.93370819091797, "time": 0.14556424617767333, "iter": 50, "memory": 3233, "step": 50} +{"base_lr": 3.962697798532355e-06, "lr": 3.962697798532355e-06, "data_time": 0.00457608699798584, "loss": 2.5395385384559632, "decode.loss_ce": 2.5395385384559632, "decode.acc_seg": 95.17093658447266, "time": 0.14469559192657472, "iter": 100, "memory": 1338, "step": 100} +{"base_lr": 5.964030020013346e-06, "lr": 5.964030020013346e-06, 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b/my_projects/conversion_tests/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512.py new file mode 100644 index 0000000000..6491441345 --- /dev/null +++ b/my_projects/conversion_tests/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'https://download.openmmlab.com/mmsegmentation/v0.5/segnext/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k/segnext_mscan-t_1x16_512x512_adamw_160k_ade20k_20230210_140244-05bd8466.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=31, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/segnext_mscan-t_1xb2-pre-ade20k-10k_SODHOTS-C-512x512' diff --git a/my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20241029_055416/20241029_055416.json b/my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20241029_055416/20241029_055416.json new file mode 100644 index 0000000000..4d29bda2e3 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20241029_055416/20241029_055416.json @@ -0,0 +1 @@ +{"mPr@50": 35.48, "mPr@60": 29.82, "mPr@70": 23.15, "mPr@80": 11.29, "mPr@90": 2.3, "mIoU": 24.77, "data_time": 0.9661981752603361, "time": 1.2024022966328234} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_arid10_arid20_arid10.py b/my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_arid10_arid20_arid10.py new file mode 100644 index 0000000000..073bc8a468 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_arid10_arid20_arid10.py @@ -0,0 +1,345 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='ARID20', + output_dir= + 'my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/pred_results', + target_dataset='ARID10', + test_dataset='ARID10', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ARID20', + save_dir= + 'my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/show', + target_dataset='ARID10', + test_dataset='ARID10', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_label_results.json b/my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_label_results.json new file mode 100644 index 0000000000..8eac2c9216 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_label_results.json @@ -0,0 +1,258 @@ +{ + "_background_": { + "Pr@50": 21.78, + "Pr@60": 18.81, + "Pr@70": 18.81, + "Pr@80": 18.81, + "Pr@90": 18.81, + "IoU": 43.24 + }, + "apple": { + "Pr@50": 36.36, + "Pr@60": 22.73, + "Pr@70": 13.64, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 32.33 + }, + "ball": { + "Pr@50": 87.5, + "Pr@60": 68.75, + "Pr@70": 50.0, + "Pr@80": 12.5, + "Pr@90": 0.0, + "IoU": 55.06 + }, + "banana": { + "Pr@50": 15.79, + "Pr@60": 10.53, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 14.92 + }, + "bell_pepper": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.51 + }, + "binder": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 38.89, + "Pr@90": 0.0, + "IoU": 4.5 + }, + "bowl": { + "Pr@50": NaN, + "Pr@60": NaN, + "Pr@70": NaN, + "Pr@80": NaN, + "Pr@90": NaN, + "IoU": NaN + }, + "cereal_box": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 17.07 + }, + "coffee_mug": { + "Pr@50": 25.0, + "Pr@60": 12.5, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 44.33 + }, + "flashlight": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "food_bag": { + "Pr@50": 90.0, + "Pr@60": 80.0, + "Pr@70": 76.67, + "Pr@80": 60.0, + "Pr@90": 23.33, + "IoU": 55.79 + }, + "food_box": { + "Pr@50": 20.0, + "Pr@60": 5.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 3.29 + }, + "food_can": { + "Pr@50": 25.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 23.89 + }, + "glue_stick": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "hand_towel": { + "Pr@50": 28.95, + "Pr@60": 23.68, + "Pr@70": 18.42, + "Pr@80": 18.42, + "Pr@90": 18.42, + "IoU": 4.47 + }, + "instant_noodles": { + "Pr@50": 27.78, + "Pr@60": 27.78, + "Pr@70": 27.78, + "Pr@80": 5.56, + "Pr@90": 0.0, + "IoU": 21.42 + }, + "keyboard": { + "Pr@50": 97.87, + "Pr@60": 95.74, + "Pr@70": 63.83, + "Pr@80": 34.04, + "Pr@90": 0.0, + "IoU": 71.32 + }, + "kleenex": { + "Pr@50": 79.17, + "Pr@60": 62.5, + "Pr@70": 54.17, + "Pr@80": 29.17, + "Pr@90": 8.33, + "IoU": 51.01 + }, + "lemon": { + "Pr@50": 10.0, + "Pr@60": 10.0, + "Pr@70": 10.0, + "Pr@80": 10.0, + "Pr@90": 0.0, + "IoU": 31.02 + }, + "lime": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "marker": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 15.06 + }, + "orange": { + "Pr@50": 50.0, + "Pr@60": 40.91, + "Pr@70": 22.73, + "Pr@80": 4.55, + "Pr@90": 0.0, + "IoU": 41.79 + }, + "peach": { + "Pr@50": 38.89, + "Pr@60": 11.11, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 38.85 + }, + "pear": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 4.97 + }, + "potato": { + "Pr@50": 47.06, + "Pr@60": 41.18, + "Pr@70": 23.53, + "Pr@80": 5.88, + "Pr@90": 0.0, + "IoU": 56.54 + }, + "shampoo": { + "Pr@50": 48.0, + "Pr@60": 48.0, + "Pr@70": 24.0, + "Pr@80": 12.0, + "Pr@90": 0.0, + "IoU": 36.44 + }, + "soda_can": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 88.89, + "Pr@90": 0.0, + "IoU": 64.26 + }, + "sponge": { + "Pr@50": 15.38, + "Pr@60": 15.38, + "Pr@70": 7.69, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 2.51 + }, + "stapler": { + "Pr@50": NaN, + "Pr@60": NaN, + "Pr@70": NaN, + "Pr@80": NaN, + "Pr@90": NaN, + "IoU": 0.0 + }, + "tomato": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "toothpaste": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 83.33, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 19.78 + }, + "_unknown_": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 13.4 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_results.json b/my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_results.json new file mode 100644 index 0000000000..0900f57712 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 35.48, + "mPr@60": 29.82, + "mPr@70": 23.15, + "mPr@80": 11.29, + "mPr@90": 2.3, + "mIoU": 24.77 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..4d8a8ca7e7 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4962 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.018044548974033698 + 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a/my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/confusion_matrix/top_10_confusion.json b/my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..98a2961a76 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "glue_stick", + "pred_label": "toothpaste", + "score": 98.67786806225037 + }, + { + "gt_label": "lime", + "pred_label": "apple", + "score": 95.546875 + }, + { + "gt_label": "flashlight", + "pred_label": "marker", + "score": 89.73753280839894 + }, + { + "gt_label": "_unknown_", + "pred_label": "cereal_box", + "score": 76.60219946909366 + }, + { + "gt_label": "instant_noodles", + 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b/my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/show/vis_data/config.py @@ -0,0 +1,345 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='ARID20', + output_dir= + 'my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/pred_results', + target_dataset='ARID10', + test_dataset='ARID10', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ARID20', + save_dir= + 'my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/show', + target_dataset='ARID10', + test_dataset='ARID10', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/arid202arid10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20241029_055903/20241029_055903.json b/my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20241029_055903/20241029_055903.json new file mode 100644 index 0000000000..b2e6364277 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20241029_055903/20241029_055903.json @@ -0,0 +1 @@ +{"mPr@50": 60.38, "mPr@60": 55.64, "mPr@70": 50.5, "mPr@80": 43.85, "mPr@90": 16.84, "mIoU": 43.32, "data_time": 1.0195273361583748, "time": 1.3313413539735397} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..ee946d67cb --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4962 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.018922129480842043 + }, + { + "gt_label": "_background_", + "pred_label": "ball", + "score": 0.0021696777636410554 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.03384473248171925 + }, + { + "gt_label": "_background_", + "pred_label": "bell_pepper", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "binder", + "score": 6.636745528167159 + }, + { + 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"score": 94.765625 + }, + { + "gt_label": "flashlight", + "pred_label": "marker", + "score": 87.69028871391076 + }, + { + "gt_label": "toothpaste", + "pred_label": "marker", + "score": 63.309403153153156 + }, + { + "gt_label": "pear", + "pred_label": "lemon", + "score": 56.07346382556987 + }, + { + "gt_label": "instant_noodles", + "pred_label": "cereal_box", + "score": 45.55158363122766 + }, + { + "gt_label": "food_box", + "pred_label": "sponge", + "score": 33.45934802689129 + }, + { + "gt_label": "shampoo", + "pred_label": "toothpaste", + "score": 27.419322489420168 + }, + { + "gt_label": "_unknown_", + "pred_label": "cereal_box", + "score": 23.928706863860448 + }, + { + "gt_label": "coffee_mug", + "pred_label": "soda_can", + "score": 21.597031006823897 + } +] \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/mask2former_arid10_arid20_arid10.py b/my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/mask2former_arid10_arid20_arid10.py new file mode 100644 index 0000000000..150305be03 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/mask2former_arid10_arid20_arid10.py @@ -0,0 +1,444 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='ARID20', + output_dir= + 'my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/pred_results', + target_dataset='ARID10', + test_dataset='ARID10', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ARID20', + save_dir= + 'my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/show', + target_dataset='ARID10', + test_dataset='ARID10', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/mask2former_label_results.json b/my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/mask2former_label_results.json new file mode 100644 index 0000000000..b364168289 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/mask2former_label_results.json @@ -0,0 +1,258 @@ +{ + "_background_": { + "Pr@50": 67.33, + "Pr@60": 52.48, + "Pr@70": 48.51, + "Pr@80": 38.61, + "Pr@90": 34.65, + "IoU": 64.35 + }, + "apple": { + "Pr@50": 54.55, + "Pr@60": 45.45, + "Pr@70": 45.45, + "Pr@80": 40.91, + "Pr@90": 0.0, + "IoU": 46.08 + }, + "ball": { + "Pr@50": 75.0, + "Pr@60": 75.0, + "Pr@70": 75.0, + "Pr@80": 75.0, + "Pr@90": 25.0, + "IoU": 69.11 + }, + "banana": { + "Pr@50": 100.0, + "Pr@60": 89.47, + "Pr@70": 84.21, + "Pr@80": 57.89, + "Pr@90": 0.0, + "IoU": 79.85 + }, + "bell_pepper": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "binder": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 94.44, + "Pr@90": 61.11, + "IoU": 10.17 + }, + "bowl": { + "Pr@50": NaN, + "Pr@60": NaN, + "Pr@70": NaN, + "Pr@80": NaN, + "Pr@90": NaN, + "IoU": NaN + }, + "cereal_box": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 15.42 + }, + "coffee_mug": { + "Pr@50": 62.5, + "Pr@60": 62.5, + "Pr@70": 62.5, + "Pr@80": 25.0, + "Pr@90": 0.0, + "IoU": 51.55 + }, + "flashlight": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "food_bag": { + "Pr@50": 96.67, + "Pr@60": 96.67, + "Pr@70": 96.67, + "Pr@80": 93.33, + "Pr@90": 86.67, + "IoU": 87.49 + }, + "food_box": { + "Pr@50": 55.0, + "Pr@60": 45.0, + "Pr@70": 40.0, + "Pr@80": 40.0, + "Pr@90": 35.0, + "IoU": 23.22 + }, + "food_can": { + "Pr@50": 75.0, + "Pr@60": 75.0, + "Pr@70": 75.0, + "Pr@80": 50.0, + "Pr@90": 0.0, + "IoU": 48.18 + }, + "glue_stick": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 20.0, + "IoU": 36.82 + }, + "hand_towel": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 78.95, + "IoU": 92.98 + }, + "instant_noodles": { + "Pr@50": 44.44, + "Pr@60": 44.44, + "Pr@70": 44.44, + "Pr@80": 27.78, + "Pr@90": 0.0, + "IoU": 18.33 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 93.62, + "Pr@90": 61.7, + "IoU": 90.24 + }, + "kleenex": { + "Pr@50": 95.83, + "Pr@60": 95.83, + "Pr@70": 79.17, + "Pr@80": 66.67, + "Pr@90": 41.67, + "IoU": 31.35 + }, + "lemon": { + "Pr@50": 50.0, + "Pr@60": 30.0, + "Pr@70": 10.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 45.34 + }, + "lime": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "marker": { + "Pr@50": 16.67, + "Pr@60": 16.67, + "Pr@70": 16.67, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 29.01 + }, + "orange": { + "Pr@50": 59.09, + "Pr@60": 59.09, + "Pr@70": 59.09, + "Pr@80": 59.09, + "Pr@90": 9.09, + "IoU": 56.61 + }, + "peach": { + "Pr@50": 55.56, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 44.44, + "Pr@90": 11.11, + "IoU": 46.67 + }, + "pear": { + "Pr@50": 36.36, + "Pr@60": 9.09, + "Pr@70": 9.09, + "Pr@80": 9.09, + "Pr@90": 0.0, + "IoU": 19.52 + }, + "potato": { + "Pr@50": 88.24, + "Pr@60": 82.35, + "Pr@70": 58.82, + "Pr@80": 47.06, + "Pr@90": 5.88, + "IoU": 70.71 + }, + "shampoo": { + "Pr@50": 44.0, + "Pr@60": 44.0, + "Pr@70": 44.0, + "Pr@80": 44.0, + "Pr@90": 12.0, + "IoU": 35.59 + }, + "soda_can": { + "Pr@50": 100.0, + "Pr@60": 77.78, + "Pr@70": 77.78, + "Pr@80": 77.78, + "Pr@90": 22.22, + "IoU": 77.26 + }, + "sponge": { + "Pr@50": 38.46, + "Pr@60": 38.46, + "Pr@70": 38.46, + "Pr@80": 30.77, + "Pr@90": 0.0, + "IoU": 6.41 + }, + "stapler": { + "Pr@50": NaN, + "Pr@60": NaN, + "Pr@70": NaN, + "Pr@80": NaN, + "Pr@90": NaN, + "IoU": NaN + }, + "tomato": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 0.0, + "IoU": 84.19 + }, + "toothpaste": { + "Pr@50": 16.67, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 14.72 + }, + "_unknown_": { + "Pr@50": 80.0, + "Pr@60": 80.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 48.55 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/mask2former_results.json b/my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/mask2former_results.json new file mode 100644 index 0000000000..b1c3714c37 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/mask2former_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 60.38, + "mPr@60": 55.64, + "mPr@70": 50.5, + "mPr@80": 43.85, + "mPr@90": 16.84, + "mIoU": 43.32 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..150305be03 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/show/vis_data/config.py @@ -0,0 +1,444 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='ARID20', + output_dir= + 'my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/pred_results', + target_dataset='ARID10', + test_dataset='ARID10', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ARID20', + save_dir= + 'my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/show', + target_dataset='ARID10', + test_dataset='ARID10', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/arid202arid10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20241029_060405/20241029_060405.json b/my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20241029_060405/20241029_060405.json new file mode 100644 index 0000000000..2994eb2fb7 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20241029_060405/20241029_060405.json @@ -0,0 +1 @@ +{"mPr@50": 56.39, "mPr@60": 50.81, "mPr@70": 42.05, "mPr@80": 34.7, "mPr@90": 11.54, "mIoU": 43.9, "data_time": 0.9857656719660995, "time": 1.2647464180936907} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..cf837090eb --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4962 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.015366994831984412 + }, + { + "gt_label": "_background_", + "pred_label": "ball", + "score": 0.008107350128854959 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.0516540151922256 + }, + { + "gt_label": "_background_", + "pred_label": "bell_pepper", + "score": 0.0016281919190146302 + }, + { + "gt_label": "_background_", + "pred_label": 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"gt_label": "_unknown_", + "pred_label": "toothpaste", + "score": 0.0 + } +] \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/confusion_matrix/top_10_confusion.json b/my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..10c0e3c1b4 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "lime", + "pred_label": "apple", + "score": 93.59375 + }, + { + "gt_label": "flashlight", + "pred_label": "marker", + "score": 76.9028871391076 + }, + { + "gt_label": "pear", + "pred_label": "lemon", + "score": 70.40696233894946 + }, + { + "gt_label": "coffee_mug", + "pred_label": "soda_can", + "score": 57.003471806536574 + }, + { + "gt_label": "potato", + "pred_label": "lemon", + "score": 55.890858016276276 + }, + { + "gt_label": "bell_pepper", + "pred_label": "orange", + "score": 51.95252679938744 + }, + { + "gt_label": "sponge", + "pred_label": "food_box", + "score": 49.15346507703195 + }, + { + "gt_label": "food_box", + "pred_label": "food_bag", + "score": 30.977592629975593 + }, + { + "gt_label": "bell_pepper", + "pred_label": "apple", + "score": 24.387442572741193 + }, + { + "gt_label": "pear", + "pred_label": "apple", + "score": 23.525768087215067 + } +] \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_arid10_arid20_arid10.py b/my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_arid10_arid20_arid10.py new file mode 100644 index 0000000000..a4c2e7e88c --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_arid10_arid20_arid10.py @@ -0,0 +1,534 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='ARID20', + output_dir= + 'my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/pred_results', + target_dataset='ARID10', + test_dataset='ARID10', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ARID20', + save_dir= + 'my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/show', + target_dataset='ARID10', + test_dataset='ARID10', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_label_results.json b/my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_label_results.json new file mode 100644 index 0000000000..e4840be389 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_label_results.json @@ -0,0 +1,258 @@ +{ + "_background_": { + "Pr@50": 34.65, + "Pr@60": 34.65, + "Pr@70": 34.65, + "Pr@80": 34.65, + "Pr@90": 34.65, + "IoU": 53.47 + }, + "apple": { + "Pr@50": 81.82, + "Pr@60": 81.82, + "Pr@70": 72.73, + "Pr@80": 54.55, + "Pr@90": 4.55, + "IoU": 61.01 + }, + "ball": { + "Pr@50": 87.5, + "Pr@60": 81.25, + "Pr@70": 81.25, + "Pr@80": 75.0, + "Pr@90": 37.5, + "IoU": 71.14 + }, + "banana": { + "Pr@50": 94.74, + "Pr@60": 78.95, + "Pr@70": 63.16, + "Pr@80": 47.37, + "Pr@90": 0.0, + "IoU": 75.15 + }, + "bell_pepper": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "binder": { + "Pr@50": 100.0, + "Pr@60": 77.78, + "Pr@70": 61.11, + "Pr@80": 50.0, + "Pr@90": 0.0, + "IoU": 4.57 + }, + "bowl": { + "Pr@50": NaN, + "Pr@60": NaN, + "Pr@70": NaN, + "Pr@80": NaN, + "Pr@90": NaN, + "IoU": 0.0 + }, + "cereal_box": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 25.38 + }, + "coffee_mug": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "flashlight": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 2.15 + }, + "food_bag": { + "Pr@50": 73.33, + "Pr@60": 73.33, + "Pr@70": 73.33, + "Pr@80": 60.0, + "Pr@90": 50.0, + "IoU": 60.37 + }, + "food_box": { + "Pr@50": 20.0, + "Pr@60": 20.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 4.04 + }, + "food_can": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 45.67 + }, + "glue_stick": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 20.0, + "IoU": 87.16 + }, + "hand_towel": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 71.05, + "IoU": 91.0 + }, + "instant_noodles": { + "Pr@50": 55.56, + "Pr@60": 27.78, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 48.94 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 91.49, + "Pr@90": 46.81, + "IoU": 89.39 + }, + "kleenex": { + "Pr@50": 87.5, + "Pr@60": 83.33, + "Pr@70": 75.0, + "Pr@80": 41.67, + "Pr@90": 33.33, + "IoU": 77.19 + }, + "lemon": { + "Pr@50": 10.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 28.57 + }, + "lime": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "marker": { + "Pr@50": 83.33, + "Pr@60": 83.33, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 50.49 + }, + "orange": { + "Pr@50": 72.73, + "Pr@60": 68.18, + "Pr@70": 68.18, + "Pr@80": 63.64, + "Pr@90": 18.18, + "IoU": 53.31 + }, + "peach": { + "Pr@50": 61.11, + "Pr@60": 61.11, + "Pr@70": 55.56, + "Pr@80": 44.44, + "Pr@90": 22.22, + "IoU": 67.22 + }, + "pear": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.16 + }, + "potato": { + "Pr@50": 29.41, + "Pr@60": 29.41, + "Pr@70": 29.41, + "Pr@80": 23.53, + "Pr@90": 0.0, + "IoU": 21.31 + }, + "shampoo": { + "Pr@50": 76.0, + "Pr@60": 76.0, + "Pr@70": 72.0, + "Pr@80": 44.0, + "Pr@90": 8.0, + "IoU": 63.39 + }, + "soda_can": { + "Pr@50": 88.89, + "Pr@60": 88.89, + "Pr@70": 66.67, + "Pr@80": 22.22, + "Pr@90": 0.0, + "IoU": 63.72 + }, + "sponge": { + "Pr@50": 38.46, + "Pr@60": 38.46, + "Pr@70": 38.46, + "Pr@80": 38.46, + "Pr@90": 0.0, + "IoU": 27.94 + }, + "stapler": { + "Pr@50": NaN, + "Pr@60": NaN, + "Pr@70": NaN, + "Pr@80": NaN, + "Pr@90": NaN, + "IoU": NaN + }, + "tomato": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 0.0, + "IoU": 83.59 + }, + "toothpaste": { + "Pr@50": 66.67, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 0.0, + "IoU": 48.58 + }, + "_unknown_": { + "Pr@50": 80.0, + "Pr@60": 20.0, + "Pr@70": 20.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 55.87 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_results.json b/my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_results.json new file mode 100644 index 0000000000..82f602a698 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 56.39, + "mPr@60": 50.81, + "mPr@70": 42.05, + "mPr@80": 34.7, + "mPr@90": 11.54, + "mIoU": 43.9 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..a4c2e7e88c --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/show/vis_data/config.py @@ -0,0 +1,534 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='ARID20', + output_dir= + 'my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/pred_results', + target_dataset='ARID10', + test_dataset='ARID10', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ARID20', + save_dir= + 'my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/show', + target_dataset='ARID10', + test_dataset='ARID10', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/arid202arid10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/20241029_060850/20241029_060850.json b/my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/20241029_060850/20241029_060850.json new file mode 100644 index 0000000000..0856885bed --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/20241029_060850/20241029_060850.json @@ -0,0 +1 @@ +{"mPr@50": 50.16, "mPr@60": 38.01, "mPr@70": 23.88, "mPr@80": 9.08, "mPr@90": 1.59, "mIoU": 34.53, "data_time": 0.9660806773912789, "time": 1.2080090518044952} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..5944a97593 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4962 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.05943647381430471 + }, + { + "gt_label": "_background_", + "pred_label": "ball", + "score": 0.012752925237236151 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.1667739057598036 + }, + { + "gt_label": "_background_", + "pred_label": 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11.149032992036405 + }, + { + "gt_label": "_unknown_", + "pred_label": "lemon", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "lime", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "marker", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "orange", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "peach", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "pear", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "potato", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "shampoo", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "soda_can", + "score": 0.23294869711251964 + }, + { + "gt_label": "_unknown_", + "pred_label": "sponge", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "stapler", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "tomato", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "toothpaste", + "score": 2.8658107156400674 + } +] \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/confusion_matrix/top_10_confusion.json b/my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..06037f4d28 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "lime", + "pred_label": "apple", + "score": 99.921875 + }, + { + "gt_label": "flashlight", + "pred_label": "marker", + "score": 80.55118110236221 + }, + { + "gt_label": "pear", + "pred_label": "lemon", + "score": 41.25061942517344 + }, + { + "gt_label": "instant_noodles", + "pred_label": "cereal_box", + "score": 38.370872184586155 + }, + { + "gt_label": "pear", + "pred_label": "apple", + "score": 33.987859266600594 + }, + { + "gt_label": "lemon", + "pred_label": "apple", + "score": 29.401053880321133 + }, + { + "gt_label": "potato", + "pred_label": "food_bag", + "score": 28.08485148923865 + }, + { + "gt_label": "glue_stick", + "pred_label": "ball", + "score": 27.55818757746867 + }, + { + "gt_label": "sponge", + "pred_label": "cereal_box", + "score": 27.55729762099189 + }, + { + "gt_label": "shampoo", + "pred_label": "toothpaste", + "score": 27.01618564351471 + } +] \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/segformer_arid10_arid20_arid10.py b/my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/segformer_arid10_arid20_arid10.py new file mode 100644 index 0000000000..8ce2d00376 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/segformer_arid10_arid20_arid10.py @@ -0,0 +1,331 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='ARID20', + output_dir= + 'my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/pred_results', + target_dataset='ARID10', + test_dataset='ARID10', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ARID20', + save_dir= + 'my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/show', + target_dataset='ARID10', + test_dataset='ARID10', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/segformer_label_results.json b/my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/segformer_label_results.json new file mode 100644 index 0000000000..7be7e4e5a6 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/segformer_label_results.json @@ -0,0 +1,258 @@ +{ + "_background_": { + "Pr@50": 34.65, + "Pr@60": 34.65, + "Pr@70": 34.65, + "Pr@80": 34.65, + "Pr@90": 34.65, + "IoU": 55.92 + }, + "apple": { + "Pr@50": 59.09, + "Pr@60": 27.27, + "Pr@70": 18.18, + "Pr@80": 13.64, + "Pr@90": 0.0, + "IoU": 38.27 + }, + "ball": { + "Pr@50": 56.25, + "Pr@60": 56.25, + "Pr@70": 31.25, + "Pr@80": 25.0, + "Pr@90": 0.0, + "IoU": 42.45 + }, + "banana": { + "Pr@50": 68.42, + "Pr@60": 68.42, + "Pr@70": 57.89, + "Pr@80": 5.26, + "Pr@90": 0.0, + "IoU": 40.31 + }, + "bell_pepper": { + "Pr@50": 100.0, + "Pr@60": 50.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 56.55 + }, + "binder": { + "Pr@50": 55.56, + "Pr@60": 50.0, + "Pr@70": 27.78, + "Pr@80": 11.11, + "Pr@90": 0.0, + "IoU": 4.2 + }, + "bowl": { + "Pr@50": NaN, + "Pr@60": NaN, + "Pr@70": NaN, + "Pr@80": NaN, + "Pr@90": NaN, + "IoU": 0.0 + }, + "cereal_box": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 23.64 + }, + "coffee_mug": { + "Pr@50": 25.0, + "Pr@60": 25.0, + "Pr@70": 25.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 42.06 + }, + "flashlight": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 7.38 + }, + "food_bag": { + "Pr@50": 86.67, + "Pr@60": 73.33, + "Pr@70": 73.33, + "Pr@80": 3.33, + "Pr@90": 0.0, + "IoU": 51.35 + }, + "food_box": { + "Pr@50": 10.0, + "Pr@60": 5.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 6.84 + }, + "food_can": { + "Pr@50": 25.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 10.22 + }, + "glue_stick": { + "Pr@50": 60.0, + "Pr@60": 20.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 30.95 + }, + "hand_towel": { + "Pr@50": 76.32, + "Pr@60": 50.0, + "Pr@70": 39.47, + "Pr@80": 31.58, + "Pr@90": 2.63, + "IoU": 63.35 + }, + "instant_noodles": { + "Pr@50": 61.11, + "Pr@60": 44.44, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 43.89 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 85.11, + "Pr@70": 72.34, + "Pr@80": 68.09, + "Pr@90": 2.13, + "IoU": 76.02 + }, + "kleenex": { + "Pr@50": 70.83, + "Pr@60": 70.83, + "Pr@70": 41.67, + "Pr@80": 20.83, + "Pr@90": 8.33, + "IoU": 56.95 + }, + "lemon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 20.76 + }, + "lime": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "marker": { + "Pr@50": 66.67, + "Pr@60": 16.67, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 34.4 + }, + "orange": { + "Pr@50": 77.27, + "Pr@60": 72.73, + "Pr@70": 31.82, + "Pr@80": 13.64, + "Pr@90": 0.0, + "IoU": 49.43 + }, + "peach": { + "Pr@50": 50.0, + "Pr@60": 44.44, + "Pr@70": 27.78, + "Pr@80": 11.11, + "Pr@90": 0.0, + "IoU": 54.76 + }, + "pear": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.41 + }, + "potato": { + "Pr@50": 64.71, + "Pr@60": 41.18, + "Pr@70": 23.53, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 52.52 + }, + "shampoo": { + "Pr@50": 52.0, + "Pr@60": 44.0, + "Pr@70": 24.0, + "Pr@80": 12.0, + "Pr@90": 0.0, + "IoU": 42.25 + }, + "soda_can": { + "Pr@50": 100.0, + "Pr@60": 55.56, + "Pr@70": 22.22, + "Pr@80": 22.22, + "Pr@90": 0.0, + "IoU": 50.65 + }, + "sponge": { + "Pr@50": 15.38, + "Pr@60": 15.38, + "Pr@70": 15.38, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 3.22 + }, + "stapler": { + "Pr@50": NaN, + "Pr@60": NaN, + "Pr@70": NaN, + "Pr@80": NaN, + "Pr@90": NaN, + "IoU": 0.0 + }, + "tomato": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 76.25 + }, + "toothpaste": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 25.94 + }, + "_unknown_": { + "Pr@50": 40.0, + "Pr@60": 40.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 43.93 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/segformer_results.json b/my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/segformer_results.json new file mode 100644 index 0000000000..fee5cdf349 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/segformer_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 50.16, + "mPr@60": 38.01, + "mPr@70": 23.88, + "mPr@80": 9.08, + "mPr@90": 1.59, + "mIoU": 34.53 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..8ce2d00376 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/show/vis_data/config.py @@ -0,0 +1,331 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='ARID20', + output_dir= + 'my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/pred_results', + target_dataset='ARID10', + test_dataset='ARID10', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ARID20', + save_dir= + 'my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/show', + target_dataset='ARID10', + test_dataset='ARID10', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/arid202arid10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20241029_061329/20241029_061329.json b/my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20241029_061329/20241029_061329.json new file mode 100644 index 0000000000..f6b90ec639 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20241029_061329/20241029_061329.json @@ -0,0 +1 @@ +{"mPr@50": 55.94, "mPr@60": 49.16, "mPr@70": 39.55, "mPr@80": 19.47, "mPr@90": 2.78, "mIoU": 39.45, "data_time": 0.9698933728850714, "time": 1.2168104813830687} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..1f58002ea3 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4962 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.03307544914383619 + }, + { + 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62.64694728858551 + }, + { + "gt_label": "pear", + "pred_label": "lemon", + "score": 48.87574331020812 + }, + { + "gt_label": "flashlight", + "pred_label": "marker", + "score": 40.68241469816273 + }, + { + "gt_label": "instant_noodles", + "pred_label": "cereal_box", + "score": 37.39844511661625 + }, + { + "gt_label": "sponge", + "pred_label": "food_box", + "score": 33.894060645653 + }, + { + "gt_label": "lemon", + "pred_label": "apple", + "score": 33.608298617816644 + }, + { + "gt_label": "coffee_mug", + "pred_label": "shampoo", + "score": 33.52687657129175 + }, + { + "gt_label": "soda_can", + "pred_label": "shampoo", + "score": 31.942538593481988 + }, + { + "gt_label": "toothpaste", + "pred_label": "marker", + "score": 31.411880630630627 + } +] \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_arid10_arid20_arid10.py b/my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_arid10_arid20_arid10.py new file mode 100644 index 0000000000..1180f7a118 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_arid10_arid20_arid10.py @@ -0,0 +1,353 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='ARID20', + output_dir= + 'my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/pred_results', + target_dataset='ARID10', + test_dataset='ARID10', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ARID20', + save_dir= + 'my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/show', + target_dataset='ARID10', + test_dataset='ARID10', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_label_results.json b/my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_label_results.json new file mode 100644 index 0000000000..6bb91b4aa3 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_label_results.json @@ -0,0 +1,258 @@ +{ + "_background_": { + "Pr@50": 95.05, + "Pr@60": 59.41, + "Pr@70": 34.65, + "Pr@80": 34.65, + "Pr@90": 34.65, + "IoU": 73.36 + }, + "apple": { + "Pr@50": 45.45, + "Pr@60": 45.45, + "Pr@70": 36.36, + "Pr@80": 4.55, + "Pr@90": 0.0, + "IoU": 42.23 + }, + "ball": { + "Pr@50": 93.75, + "Pr@60": 81.25, + "Pr@70": 56.25, + "Pr@80": 43.75, + "Pr@90": 12.5, + "IoU": 58.4 + }, + "banana": { + "Pr@50": 100.0, + "Pr@60": 94.74, + "Pr@70": 63.16, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 45.09 + }, + "bell_pepper": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 50.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 74.56 + }, + "binder": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 55.56, + "Pr@90": 11.11, + "IoU": 7.03 + }, + "bowl": { + "Pr@50": NaN, + "Pr@60": NaN, + "Pr@70": NaN, + "Pr@80": NaN, + "Pr@90": NaN, + "IoU": 0.0 + }, + "cereal_box": { + "Pr@50": 22.22, + "Pr@60": 20.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 38.6 + }, + "coffee_mug": { + "Pr@50": 25.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 36.0 + }, + "flashlight": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 42.18 + }, + "food_bag": { + "Pr@50": 66.67, + "Pr@60": 60.0, + "Pr@70": 50.0, + "Pr@80": 46.67, + "Pr@90": 0.0, + "IoU": 53.41 + }, + "food_box": { + "Pr@50": 5.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 6.71 + }, + "food_can": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 0.0, + "IoU": 18.26 + }, + "glue_stick": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 80.0, + "Pr@80": 40.0, + "Pr@90": 0.0, + "IoU": 72.93 + }, + "hand_towel": { + "Pr@50": 71.05, + "Pr@60": 63.16, + "Pr@70": 57.89, + "Pr@80": 36.84, + "Pr@90": 10.53, + "IoU": 62.39 + }, + "instant_noodles": { + "Pr@50": 38.89, + "Pr@60": 33.33, + "Pr@70": 33.33, + "Pr@80": 16.67, + "Pr@90": 0.0, + "IoU": 18.35 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 93.62, + "Pr@80": 63.83, + "Pr@90": 2.13, + "IoU": 81.64 + }, + "kleenex": { + "Pr@50": 83.33, + "Pr@60": 75.0, + "Pr@70": 62.5, + "Pr@80": 58.33, + "Pr@90": 12.5, + "IoU": 73.1 + }, + "lemon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 20.06 + }, + "lime": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.88 + }, + "marker": { + "Pr@50": 50.0, + "Pr@60": 16.67, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 14.14 + }, + "orange": { + "Pr@50": 59.09, + "Pr@60": 54.55, + "Pr@70": 54.55, + "Pr@80": 36.36, + "Pr@90": 0.0, + "IoU": 50.02 + }, + "peach": { + "Pr@50": 38.89, + "Pr@60": 33.33, + "Pr@70": 11.11, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 46.7 + }, + "pear": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 2.47 + }, + "potato": { + "Pr@50": 94.12, + "Pr@60": 94.12, + "Pr@70": 82.35, + "Pr@80": 47.06, + "Pr@90": 0.0, + "IoU": 58.22 + }, + "shampoo": { + "Pr@50": 80.0, + "Pr@60": 64.0, + "Pr@70": 52.0, + "Pr@80": 20.0, + "Pr@90": 0.0, + "IoU": 43.27 + }, + "soda_can": { + "Pr@50": 66.67, + "Pr@60": 44.44, + "Pr@70": 33.33, + "Pr@80": 22.22, + "Pr@90": 0.0, + "IoU": 57.13 + }, + "sponge": { + "Pr@50": 23.08, + "Pr@60": 15.38, + "Pr@70": 15.38, + "Pr@80": 7.69, + "Pr@90": 0.0, + "IoU": 23.03 + }, + "stapler": { + "Pr@50": NaN, + "Pr@60": NaN, + "Pr@70": NaN, + "Pr@80": NaN, + "Pr@90": NaN, + "IoU": 0.0 + }, + "tomato": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 76.29 + }, + "toothpaste": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 34.45 + }, + "_unknown_": { + "Pr@50": 20.0, + "Pr@60": 20.0, + "Pr@70": 20.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 31.38 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_results.json b/my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_results.json new file mode 100644 index 0000000000..05e7079bb5 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 55.94, + "mPr@60": 49.16, + "mPr@70": 39.55, + "mPr@80": 19.47, + "mPr@90": 2.78, + "mIoU": 39.45 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..1180f7a118 --- /dev/null +++ b/my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/show/vis_data/config.py @@ -0,0 +1,353 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='ARID20', + output_dir= + 'my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/pred_results', + target_dataset='ARID10', + test_dataset='ARID10', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ARID20', + save_dir= + 'my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/show', + target_dataset='ARID10', + test_dataset='ARID10', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/arid202arid10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/20241010_011609/20241010_011609.json b/my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/20241010_011609/20241010_011609.json new file mode 100644 index 0000000000..90d61d25b7 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/20241010_011609/20241010_011609.json @@ -0,0 +1 @@ +{"mPr@50": 90.96, "mPr@60": 85.51, "mPr@70": 81.6, "mPr@80": 59.55, "mPr@90": 26.56, "mIoU": 79.4, "data_time": 0.9343039849225212, "time": 1.2620604599223417} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_hots_cat_hots_hots_cat.py b/my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_hots_cat_hots_hots_cat.py new file mode 100644 index 0000000000..15faadaf47 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_hots_cat_hots_hots_cat.py @@ -0,0 +1,346 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS', + output_dir= + 'my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + save_dir= + 'my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_label_results.json b/my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_label_results.json new file mode 100644 index 0000000000..faba4eec06 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_label_results.json @@ -0,0 +1,210 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.93 + }, + "apple": { + "Pr@50": 66.67, + "Pr@60": 66.67, + "Pr@70": 66.67, + "Pr@80": 33.33, + "Pr@90": 33.33, + "IoU": 72.66 + }, + "banana": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 33.33, + "Pr@90": 0.0, + "IoU": 78.31 + }, + "book": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 75.0, + "IoU": 92.44 + }, + "bowl": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 33.33, + "IoU": 88.07 + }, + "can": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 70.0, + "Pr@90": 10.0, + "IoU": 84.72 + }, + "cup": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 66.67, + "Pr@90": 0.0, + "IoU": 83.46 + }, + "fork": { + "Pr@50": 66.67, + "Pr@60": 16.67, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 51.27 + }, + "juice_box": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 57.14, + "IoU": 88.36 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 94.42 + }, + "knife": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 55.16 + }, + "laptop": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.74 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 50.0, + "Pr@90": 0.0, + "IoU": 83.02 + }, + 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b/my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 90.96, + "mPr@60": 85.51, + "mPr@70": 81.6, + "mPr@80": 59.55, + "mPr@90": 26.56, + "mIoU": 79.4 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..2163efbc86 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/confusion_matrix/confusion.json @@ -0,0 +1,3252 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.002960472332870847 + }, + { + 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"score": 0.0 + } +] \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/confusion_matrix/top_10_confusion.json b/my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..02e7381189 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "knife", + "pred_label": "pen", + "score": 9.47119179163378 + }, + { + "gt_label": "knife", + "pred_label": "fork", + "score": 7.853196527229676 + }, + { + "gt_label": "marker", + "pred_label": "pen", + "score": 6.9058295964125564 + }, + { + "gt_label": "fork", + "pred_label": "pen", + "score": 4.771846107963018 + }, + { + "gt_label": "spoon", + "pred_label": "fork", + "score": 3.8819462908800855 + }, + { + "gt_label": "scissors", + "pred_label": "mouse", + "score": 3.8492253633604854 + }, + { + "gt_label": "banana", + "pred_label": "pringles", + "score": 3.746800093088201 + }, + { + "gt_label": "fork", + "pred_label": "marker", + "score": 2.863107664777811 + }, + { + "gt_label": "scissors", + "pred_label": "pringles", + "score": 2.5235585369749245 + }, + { + "gt_label": "mouse", + "pred_label": "keyboard", + "score": 2.2045056471583018 + } +] \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..15faadaf47 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/show/vis_data/config.py @@ -0,0 +1,346 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS', + output_dir= + 'my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + save_dir= + 'my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/20241010_011712/20241010_011712.json b/my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/20241010_011712/20241010_011712.json new file mode 100644 index 0000000000..b9b73a1c0c --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/20241010_011712/20241010_011712.json @@ -0,0 +1 @@ +{"mPr@50": 96.11, "mPr@60": 96.11, "mPr@70": 95.47, "mPr@80": 86.32, "mPr@90": 59.04, "mIoU": 88.68, "data_time": 0.9434708426980412, "time": 1.3507104340721579} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..f0dfc19730 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/confusion_matrix/confusion.json @@ -0,0 +1,3252 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.003916215369466947 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.002284458965522386 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 0.08412869802622745 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.0121682406122723 + }, + { + "gt_label": "_background_", + "pred_label": "can", + "score": 0.05692498769189455 + }, + { + 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b/my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_hots_cat_hots_hots_cat.py new file mode 100644 index 0000000000..416f18e9e2 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_hots_cat_hots_hots_cat.py @@ -0,0 +1,460 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS', + output_dir= + 'my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + save_dir= + 'my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_label_results.json b/my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_label_results.json new file mode 100644 index 0000000000..35924cfeb4 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_label_results.json @@ -0,0 +1,210 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 98.77 + }, + "apple": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 0.0, + "IoU": 87.52 + }, + "banana": { + "Pr@50": 66.67, + "Pr@60": 66.67, + "Pr@70": 66.67, + "Pr@80": 33.33, + "Pr@90": 33.33, + "IoU": 69.44 + }, + "book": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.62 + }, + "bowl": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 83.33, + "IoU": 92.7 + }, + "can": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 92.27 + }, + "cup": { + "Pr@50": 88.89, + "Pr@60": 88.89, + "Pr@70": 88.89, + "Pr@80": 77.78, + "Pr@90": 77.78, + "IoU": 86.77 + }, + "fork": { + "Pr@50": 83.33, + "Pr@60": 83.33, + "Pr@70": 83.33, + "Pr@80": 50.0, + "Pr@90": 0.0, + "IoU": 76.0 + }, + "juice_box": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 85.71, + "IoU": 93.68 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.05 + }, + "knife": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 50.0, + "IoU": 88.14 + }, + "laptop": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.98 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 50.0, + "IoU": 90.54 + }, + "marker": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 40.0, + "Pr@90": 0.0, + "IoU": 81.05 + }, + "milk": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 94.08 + }, + "monitor": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.14 + }, + "mouse": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 80.0, + "IoU": 90.99 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 50.0, + "IoU": 90.17 + }, + "peach": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 66.67, + "IoU": 90.18 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 50.0, + "IoU": 90.61 + }, + "pen": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 83.33, + "Pr@80": 33.33, + "Pr@90": 0.0, + "IoU": 76.81 + }, + "plate": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.14 + }, + "pringles": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 83.33, + "IoU": 92.37 + }, + "scissors": { + "Pr@50": 60.0, + "Pr@60": 60.0, + "Pr@70": 60.0, + "Pr@80": 60.0, + "Pr@90": 0.0, + "IoU": 71.03 + }, + "spoon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 50.0, + "Pr@90": 0.0, + "IoU": 81.66 + }, + "stapler": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 25.0, + "IoU": 89.91 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_results.json b/my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_results.json new file mode 100644 index 0000000000..c84d0ce1d6 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 96.11, + "mPr@60": 96.11, + "mPr@70": 95.47, + "mPr@80": 86.32, + "mPr@90": 59.04, + "mIoU": 88.68 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..416f18e9e2 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/config.py @@ -0,0 +1,460 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS', + output_dir= + 'my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + save_dir= + 'my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/20241010_011815/20241010_011815.json b/my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/20241010_011815/20241010_011815.json new file mode 100644 index 0000000000..fa4345bf2d --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/20241010_011815/20241010_011815.json @@ -0,0 +1 @@ +{"mPr@50": 92.55, "mPr@60": 91.27, "mPr@70": 89.73, "mPr@80": 78.45, "mPr@90": 59.01, "mIoU": 84.42, "data_time": 0.912557882421157, "time": 1.2788569366230684} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..c68225f357 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/confusion_matrix/confusion.json @@ -0,0 +1,3252 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.0018881752674215638 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.004522296319503499 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 0.06459424279043399 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.013706753793134316 + }, + { + "gt_label": "_background_", + "pred_label": "can", + "score": 0.03391722239627624 + }, + { + "gt_label": "_background_", + "pred_label": "cup", + "score": 0.026014859240030434 + }, + { + "gt_label": "_background_", + 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a/my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_hots_cat_hots_hots_cat.py b/my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_hots_cat_hots_hots_cat.py new file mode 100644 index 0000000000..521dcf0c46 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_hots_cat_hots_hots_cat.py @@ -0,0 +1,550 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS', + output_dir= + 'my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + save_dir= + 'my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_label_results.json b/my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_label_results.json new file mode 100644 index 0000000000..6032f12f91 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_label_results.json @@ -0,0 +1,210 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 98.36 + }, + "apple": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 33.33, + "IoU": 88.11 + }, + "banana": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 66.67, + "Pr@90": 33.33, + "IoU": 85.9 + }, + "book": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 94.53 + }, + "bowl": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 83.33, + "IoU": 92.8 + }, + "can": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 90.0, + "IoU": 91.52 + }, + "cup": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 88.89, + "IoU": 92.16 + }, + "fork": { + "Pr@50": 83.33, + "Pr@60": 66.67, + "Pr@70": 66.67, + "Pr@80": 33.33, + "Pr@90": 0.0, + "IoU": 65.66 + }, + "juice_box": { + "Pr@50": 71.43, + "Pr@60": 71.43, + "Pr@70": 71.43, + "Pr@80": 71.43, + "Pr@90": 57.14, + "IoU": 74.25 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.61 + }, + "knife": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 50.0, + "IoU": 66.72 + }, + "laptop": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.21 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 50.0, + "IoU": 85.59 + }, + "marker": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 60.0, + "Pr@80": 40.0, + "Pr@90": 0.0, + "IoU": 71.33 + }, + "milk": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 92.52 + }, + "monitor": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.59 + }, + "mouse": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 80.0, + "Pr@90": 60.0, + "IoU": 86.32 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 50.0, + "IoU": 91.03 + }, + "peach": { + "Pr@50": 66.67, + "Pr@60": 66.67, + "Pr@70": 66.67, + "Pr@80": 66.67, + "Pr@90": 33.33, + "IoU": 49.53 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 93.13 + }, + "pen": { + "Pr@50": 100.0, + "Pr@60": 83.33, + "Pr@70": 83.33, + "Pr@80": 16.67, + "Pr@90": 0.0, + "IoU": 73.37 + }, + "plate": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 80.0, + "IoU": 93.66 + }, + "pringles": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 94.11 + }, + "scissors": { + "Pr@50": 60.0, + "Pr@60": 60.0, + "Pr@70": 60.0, + "Pr@80": 40.0, + "Pr@90": 0.0, + "IoU": 69.52 + }, + "spoon": { + "Pr@50": 75.0, + "Pr@60": 75.0, + "Pr@70": 75.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 68.29 + }, + "stapler": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 75.0, + "Pr@90": 25.0, + "IoU": 86.09 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_results.json b/my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_results.json new file mode 100644 index 0000000000..c28b7665c5 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 92.55, + "mPr@60": 91.27, + "mPr@70": 89.73, + "mPr@80": 78.45, + "mPr@90": 59.01, + "mIoU": 84.42 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..521dcf0c46 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/config.py @@ -0,0 +1,550 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS', + output_dir= + 'my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + save_dir= + 'my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/20241010_011919/20241010_011919.json b/my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/20241010_011919/20241010_011919.json new file mode 100644 index 0000000000..d207dd2a10 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/20241010_011919/20241010_011919.json @@ -0,0 +1 @@ +{"mPr@50": 95.9, "mPr@60": 91.28, "mPr@70": 87.82, "mPr@80": 72.35, "mPr@90": 31.14, "mIoU": 83.1, "data_time": 0.9392674670499914, "time": 1.2888498867259306} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..8d83477199 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/confusion_matrix/confusion.json @@ -0,0 +1,3252 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.002610810246311298 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + 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a/my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/confusion_matrix/top_10_confusion.json b/my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..81c47c4193 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "banana", + "pred_label": "pringles", + "score": 17.291133348848035 + }, + { + "gt_label": "spoon", + "pred_label": "fork", + "score": 8.02977931401223 + }, + { + "gt_label": "knife", + "pred_label": "fork", + "score": 5.169692186266772 + }, + { + "gt_label": "peach", + "pred_label": "lemon", + "score": 4.764280369169368 + }, + { + "gt_label": "laptop", + "pred_label": "monitor", + "score": 3.897688325975656 + }, + { + "gt_label": "milk", + "pred_label": "book", + "score": 3.8105341629153884 + }, + { + "gt_label": "fork", + "pred_label": "pen", + "score": 2.907843722039964 + }, + { + "gt_label": "scissors", + "pred_label": "knife", + "score": 2.4916147580258743 + }, + { + "gt_label": "mouse", + "pred_label": "can", + "score": 1.848160898713535 + }, + { + "gt_label": "scissors", + "pred_label": "pringles", + "score": 1.7089921737741574 + } +] \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_hots_cat_hots_hots_cat.py b/my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_hots_cat_hots_hots_cat.py new file mode 100644 index 0000000000..480ce391d3 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_hots_cat_hots_hots_cat.py @@ -0,0 +1,332 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS', + output_dir= + 'my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + save_dir= + 'my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_label_results.json b/my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_label_results.json new file mode 100644 index 0000000000..b819af4c42 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_label_results.json @@ -0,0 +1,210 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 98.3 + }, + "apple": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 66.67, + "Pr@90": 33.33, + "IoU": 86.02 + }, + "banana": { + "Pr@50": 66.67, + "Pr@60": 66.67, + "Pr@70": 66.67, + "Pr@80": 33.33, + "Pr@90": 0.0, + "IoU": 67.11 + }, + "book": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 92.68 + }, + "bowl": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 33.33, + "IoU": 87.47 + }, + "can": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 40.0, + "IoU": 87.08 + }, + "cup": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 77.78, + "Pr@90": 0.0, + "IoU": 83.44 + }, + "fork": { + "Pr@50": 66.67, + "Pr@60": 66.67, + "Pr@70": 50.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 67.13 + }, + "juice_box": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 71.43, + "IoU": 91.62 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.23 + }, + "knife": { + "Pr@50": 100.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 0.0, + "IoU": 68.31 + }, + "laptop": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 60.0, + "IoU": 91.95 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 50.0, + "Pr@90": 0.0, + "IoU": 79.52 + }, + "marker": { + "Pr@50": 80.0, + "Pr@60": 80.0, + "Pr@70": 80.0, + "Pr@80": 20.0, + "Pr@90": 0.0, + "IoU": 72.05 + }, + "milk": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 25.0, + "IoU": 86.19 + }, + "monitor": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 80.0, + "Pr@90": 80.0, + "IoU": 92.01 + }, + "mouse": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 20.0, + "IoU": 83.85 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 0.0, + "IoU": 87.94 + }, + "peach": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 66.67, + "Pr@90": 0.0, + "IoU": 82.59 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 0.0, + "IoU": 88.76 + }, + "pen": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 66.67, + "Pr@80": 16.67, + "Pr@90": 0.0, + "IoU": 71.17 + }, + "plate": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 80.0, + "IoU": 92.73 + }, + "pringles": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 66.67, + "IoU": 90.1 + }, + "scissors": { + "Pr@50": 80.0, + "Pr@60": 60.0, + "Pr@70": 20.0, + "Pr@80": 20.0, + "Pr@90": 0.0, + "IoU": 67.63 + }, + "spoon": { + "Pr@50": 100.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 64.45 + }, + "stapler": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 0.0, + "IoU": 85.33 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_results.json b/my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_results.json new file mode 100644 index 0000000000..d1d65145ed --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 95.9, + "mPr@60": 91.28, + "mPr@70": 87.82, + "mPr@80": 72.35, + "mPr@90": 31.14, + "mIoU": 83.1 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..480ce391d3 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/show/vis_data/config.py @@ -0,0 +1,332 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS', + output_dir= + 'my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + save_dir= + 'my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots2hots_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/20241010_012020/20241010_012020.json b/my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/20241010_012020/20241010_012020.json new file mode 100644 index 0000000000..52e1a95136 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/20241010_012020/20241010_012020.json @@ -0,0 +1 @@ +{"mPr@50": 97.31, "mPr@60": 96.54, "mPr@70": 90.71, "mPr@80": 79.1, "mPr@90": 47.57, "mIoU": 86.42, "data_time": 0.919484068365658, "time": 1.2606886134428137} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..c1fbded9dd --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/confusion_matrix/confusion.json @@ -0,0 +1,3252 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.0034966208655954883 + }, + { + "gt_label": 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"_background_", + "score": 6.593175853018372 + }, + { + "gt_label": "stapler", + "pred_label": "apple", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "banana", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "book", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "bowl", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "can", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "cup", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "fork", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "juice_box", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "keyboard", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "knife", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "laptop", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "lemon", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "marker", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "milk", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "monitor", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "mouse", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "orange", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "peach", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "pear", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "pen", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "plate", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "pringles", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "scissors", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "spoon", + "score": 0.0 + } +] \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/confusion_matrix/top_10_confusion.json b/my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..4f7a82086c --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "banana", + "pred_label": "pringles", + "score": 12.380730742378404 + }, + { + "gt_label": "mouse", + "pred_label": "can", + "score": 5.019025185722051 + }, + { + "gt_label": "scissors", + "pred_label": "pringles", + "score": 4.472129052866954 + }, + { + "gt_label": "fork", + "pred_label": "knife", + "score": 3.7130927527587234 + }, + { + "gt_label": "scissors", + "pred_label": "spoon", + "score": 3.4179843475483147 + }, + { + "gt_label": "knife", + "pred_label": "fork", + "score": 1.6969218626677192 + }, + { + "gt_label": "plate", + "pred_label": "juice_box", + "score": 1.2625006432578005 + }, + { + "gt_label": "mouse", + "pred_label": "keyboard", + "score": 0.8878420003623846 + }, + { + "gt_label": "plate", + "pred_label": "bowl", + "score": 0.7839168396315419 + }, + { + "gt_label": "can", + "pred_label": "cup", + "score": 0.5431063031239651 + } +] \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_hots_cat_hots_hots_cat.py b/my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_hots_cat_hots_hots_cat.py new file mode 100644 index 0000000000..23a5a56903 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_hots_cat_hots_hots_cat.py @@ -0,0 +1,354 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=47, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS', + output_dir= + 'my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + save_dir= + 'my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_label_results.json b/my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_label_results.json new file mode 100644 index 0000000000..cc6681ab3e --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_label_results.json @@ -0,0 +1,210 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 98.64 + }, + "apple": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 0.0, + "IoU": 86.6 + }, + "banana": { + "Pr@50": 66.67, + "Pr@60": 66.67, + "Pr@70": 66.67, + "Pr@80": 33.33, + "Pr@90": 33.33, + "IoU": 73.75 + }, + "book": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 94.77 + }, + "bowl": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 83.33, + "IoU": 89.61 + }, + "can": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 40.0, + "IoU": 89.51 + }, + "cup": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 44.44, + "IoU": 88.34 + }, + "fork": { + "Pr@50": 83.33, + "Pr@60": 83.33, + "Pr@70": 66.67, + "Pr@80": 16.67, + "Pr@90": 0.0, + "IoU": 71.77 + }, + "juice_box": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 85.71, + "IoU": 93.15 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.14 + }, + "knife": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 50.0, + "IoU": 77.39 + }, + "laptop": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.24 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 0.0, + "IoU": 88.98 + }, + "marker": { + "Pr@50": 100.0, + "Pr@60": 80.0, + "Pr@70": 80.0, + "Pr@80": 40.0, + "Pr@90": 0.0, + "IoU": 74.05 + }, + "milk": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 92.78 + }, + "monitor": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.8 + }, + "mouse": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 80.0, + "Pr@80": 80.0, + "Pr@90": 20.0, + "IoU": 84.8 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 25.0, + "IoU": 87.76 + }, + "peach": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 66.67, + "IoU": 90.1 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 0.0, + "IoU": 87.62 + }, + "pen": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 16.67, + "Pr@90": 0.0, + "IoU": 76.39 + }, + "plate": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 80.0, + "IoU": 93.17 + }, + "pringles": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 83.33, + "IoU": 92.59 + }, + "scissors": { + "Pr@50": 80.0, + "Pr@60": 80.0, + "Pr@70": 40.0, + "Pr@80": 20.0, + "Pr@90": 0.0, + "IoU": 71.13 + }, + "spoon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 75.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 67.64 + }, + "stapler": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 25.0, + "IoU": 87.26 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_results.json b/my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_results.json new file mode 100644 index 0000000000..ce53fc4e24 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 97.31, + "mPr@60": 96.54, + "mPr@70": 90.71, + "mPr@80": 79.1, + "mPr@90": 47.57, + "mIoU": 86.42 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..23a5a56903 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/config.py @@ -0,0 +1,354 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=47, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS', + output_dir= + 'my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + save_dir= + 'my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/20241010_133323/20241010_133323.json b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/20241010_133323/20241010_133323.json new file mode 100644 index 0000000000..e4e47142c8 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/20241010_133323/20241010_133323.json @@ -0,0 +1 @@ +{"mPr@50": 20.62, "mPr@60": 18.03, "mPr@70": 11.78, "mPr@80": 8.35, "mPr@90": 3.49, "mIoU": 15.26, "data_time": 1.0347739571616763, "time": 1.336225844564892} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_irl_vision_cat_hots_irl_vision_cat.py b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_irl_vision_cat_hots_irl_vision_cat.py new file mode 100644 index 0000000000..e5eb3d5a2d --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_irl_vision_cat_hots_irl_vision_cat.py @@ -0,0 +1,346 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS', + output_dir= + 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + save_dir= + 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_label_results.json b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_label_results.json new file mode 100644 index 0000000000..d53f6616af --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_label_results.json @@ -0,0 +1,242 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 78.57, + "Pr@70": 14.29, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 64.45 + }, + "apple": { + "Pr@50": 20.0, + "Pr@60": 20.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 7.47 + }, + "banana": { + "Pr@50": 60.0, + "Pr@60": 60.0, + "Pr@70": 60.0, + "Pr@80": 20.0, + "Pr@90": 0.0, + "IoU": 62.52 + }, + "book": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 1.78 + }, + "bowl": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 2.44 + }, + 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a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_results.json b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_results.json new file mode 100644 index 0000000000..28b909aa6f --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 20.62, + "mPr@60": 18.03, + "mPr@70": 11.78, + "mPr@80": 8.35, + "mPr@90": 3.49, + "mIoU": 15.26 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/confusion_matrix/confusion.json 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b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "milk", + "pred_label": "book", + "score": 96.61720121270146 + }, + { + "gt_label": "orange", + "pred_label": "apple", + "score": 79.86518491528511 + }, + { + "gt_label": "monitor", + "pred_label": "laptop", + "score": 69.07403575237579 + }, + { + "gt_label": "cereal", + "pred_label": "book", + "score": 68.44226091796851 + }, + { + "gt_label": "apple", + "pred_label": "orange", + "score": 64.59596240476057 + }, + { + "gt_label": "cup", + "pred_label": "book", + "score": 58.93414052467437 + }, + { + "gt_label": "pen", + "pred_label": "book", + "score": 55.84950029394474 + }, + { + "gt_label": "laptop", + "pred_label": "monitor", + "score": 50.08821750235247 + }, + { + "gt_label": "pear", + "pred_label": "can", + "score": 43.39438987700204 + }, + { + "gt_label": "mouse", + "pred_label": "spoon", + "score": 41.267235718975705 + } +] \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..e5eb3d5a2d --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/show/vis_data/config.py @@ -0,0 +1,346 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS', + output_dir= + 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + save_dir= + 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/20241010_133734/20241010_133734.json b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/20241010_133734/20241010_133734.json new file mode 100644 index 0000000000..65d63edf69 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/20241010_133734/20241010_133734.json @@ -0,0 +1 @@ +{"mPr@50": 34.01, "mPr@60": 32.98, "mPr@70": 31.51, "mPr@80": 30.41, "mPr@90": 25.68, "mIoU": 20.0, "data_time": 1.0336677176611764, "time": 1.395470176424299} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..3f16bbba67 --- /dev/null +++ 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0000000000..d4769fab72 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_irl_vision_cat_hots_irl_vision_cat.py @@ -0,0 +1,460 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS', + output_dir= + 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + save_dir= + 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_label_results.json b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_label_results.json new file mode 100644 index 0000000000..499ab8e5b1 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_label_results.json @@ -0,0 +1,242 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 99.57 + }, + "apple": { + "Pr@50": 40.0, + "Pr@60": 40.0, + "Pr@70": 20.0, + "Pr@80": 20.0, + "Pr@90": 20.0, + "IoU": 8.14 + }, + "banana": { + "Pr@50": 60.0, + "Pr@60": 60.0, + "Pr@70": 60.0, + "Pr@80": 60.0, + "Pr@90": 40.0, + "IoU": 43.45 + }, + "book": { + "Pr@50": 55.17, + "Pr@60": 55.17, + "Pr@70": 51.72, + "Pr@80": 48.28, + "Pr@90": 48.28, + "IoU": 54.13 + }, + "bowl": { + "Pr@50": 3.12, + "Pr@60": 3.12, + "Pr@70": 3.12, + "Pr@80": 3.12, + "Pr@90": 0.0, + "IoU": 5.14 + }, + "can": { + "Pr@50": 80.95, + "Pr@60": 80.95, + "Pr@70": 71.43, + "Pr@80": 71.43, + "Pr@90": 61.9, + "IoU": 35.34 + }, + "cap": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cereal": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cup": { + "Pr@50": 40.91, + "Pr@60": 40.91, + "Pr@70": 40.91, + "Pr@80": 40.91, + "Pr@90": 31.82, + "IoU": 22.25 + }, + "flashlight": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "juice_box": { + "Pr@50": 88.89, + "Pr@60": 88.89, + "Pr@70": 88.89, + "Pr@80": 88.89, + "Pr@90": 88.89, + "IoU": 43.9 + }, + "keyboard": { + "Pr@50": 83.33, + "Pr@60": 83.33, + "Pr@70": 83.33, + "Pr@80": 83.33, + "Pr@90": 83.33, + "IoU": 74.57 + }, + "knife": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "laptop": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "lemon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "marker": { + "Pr@50": 21.43, + "Pr@60": 21.43, + "Pr@70": 21.43, + "Pr@80": 14.29, + "Pr@90": 0.0, + "IoU": 6.21 + }, + "milk": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 25.0, + "IoU": 5.61 + }, + "monitor": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "mouse": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 1.65 + }, + "mug": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "orange": { + "Pr@50": 25.0, + "Pr@60": 25.0, + "Pr@70": 25.0, + "Pr@80": 25.0, + "Pr@90": 25.0, + "IoU": 10.1 + }, + "peach": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 50.0, + "IoU": 52.41 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 35.16 + }, + "pen": { + "Pr@50": 77.78, + "Pr@60": 66.67, + "Pr@70": 55.56, + "Pr@80": 33.33, + "Pr@90": 0.0, + "IoU": 17.55 + }, + "plate": { + "Pr@50": 53.85, + "Pr@60": 53.85, + "Pr@70": 53.85, + "Pr@80": 53.85, + "Pr@90": 46.15, + "IoU": 50.07 + }, + "pringles": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 50.0, + "IoU": 10.78 + }, + "scissors": { + "Pr@50": 40.0, + "Pr@60": 20.0, + "Pr@70": 20.0, + "Pr@80": 20.0, + "Pr@90": 0.0, + "IoU": 24.07 + }, + "sponge": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_results.json b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_results.json new file mode 100644 index 0000000000..883773d572 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/mask2former_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 34.01, + "mPr@60": 32.98, + "mPr@70": 31.51, + "mPr@80": 30.41, + "mPr@90": 25.68, + "mIoU": 20.0 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..d4769fab72 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/config.py @@ -0,0 +1,460 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS', + output_dir= + 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + save_dir= + 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/20241010_134200/20241010_134200.json b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/20241010_134200/20241010_134200.json new file mode 100644 index 0000000000..3cae608530 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/20241010_134200/20241010_134200.json @@ -0,0 +1 @@ +{"mPr@50": 32.41, "mPr@60": 31.35, "mPr@70": 30.61, "mPr@80": 28.2, "mPr@90": 22.46, "mIoU": 19.57, "data_time": 1.023803594566527, "time": 1.3578203462419056} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..f06369183b --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4352 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.0002480835853138983 + }, + { + "gt_label": "_background_", + "pred_label": "book", + 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b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_irl_vision_cat_hots_irl_vision_cat.py @@ -0,0 +1,550 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS', + output_dir= + 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + save_dir= + 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_label_results.json b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_label_results.json new file mode 100644 index 0000000000..9cdfbeee51 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_label_results.json @@ -0,0 +1,242 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 99.18 + }, + "apple": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "banana": { + "Pr@50": 20.0, + "Pr@60": 20.0, + "Pr@70": 20.0, + "Pr@80": 20.0, + "Pr@90": 20.0, + "IoU": 34.34 + }, + "book": { + "Pr@50": 58.62, + "Pr@60": 55.17, + "Pr@70": 48.28, + "Pr@80": 41.38, + "Pr@90": 27.59, + "IoU": 52.07 + }, + "bowl": { + "Pr@50": 21.88, + "Pr@60": 18.75, + "Pr@70": 12.5, + "Pr@80": 12.5, + "Pr@90": 6.25, + "IoU": 20.61 + }, + "can": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 33.33, + "Pr@80": 28.57, + "Pr@90": 23.81, + "IoU": 26.33 + }, + "cap": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cereal": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cup": { + "Pr@50": 63.64, + "Pr@60": 63.64, + "Pr@70": 54.55, + "Pr@80": 45.45, + "Pr@90": 36.36, + "IoU": 34.4 + }, + "flashlight": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "juice_box": { + "Pr@50": 77.78, + "Pr@60": 77.78, + "Pr@70": 77.78, + "Pr@80": 55.56, + "Pr@90": 44.44, + "IoU": 38.39 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 55.57 + }, + "knife": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "laptop": { + "Pr@50": 14.29, + "Pr@60": 14.29, + "Pr@70": 14.29, + "Pr@80": 14.29, + "Pr@90": 14.29, + "IoU": 9.24 + }, + "lemon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "marker": { + "Pr@50": 7.14, + "Pr@60": 7.14, + "Pr@70": 7.14, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 2.18 + }, + "milk": { + "Pr@50": 75.0, + "Pr@60": 75.0, + "Pr@70": 75.0, + "Pr@80": 75.0, + "Pr@90": 50.0, + "IoU": 5.08 + }, + "monitor": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 50.0, + "IoU": 32.2 + }, + "mouse": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "mug": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 75.0, + "Pr@70": 75.0, + "Pr@80": 75.0, + "Pr@90": 50.0, + "IoU": 35.26 + }, + "peach": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 33.33, + "Pr@80": 33.33, + "Pr@90": 0.0, + "IoU": 9.97 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 75.0, + "IoU": 25.72 + }, + "pen": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 33.33, + "Pr@80": 11.11, + "Pr@90": 0.0, + "IoU": 19.52 + }, + "plate": { + "Pr@50": 53.85, + "Pr@60": 53.85, + "Pr@70": 53.85, + "Pr@80": 53.85, + "Pr@90": 46.15, + "IoU": 61.24 + }, + "pringles": { + "Pr@50": 30.0, + "Pr@60": 30.0, + "Pr@70": 30.0, + "Pr@80": 30.0, + "Pr@90": 30.0, + "IoU": 24.16 + }, + "scissors": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "sponge": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 1.71 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_results.json b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_results.json new file mode 100644 index 0000000000..ccb941a11b --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 32.41, + "mPr@60": 31.35, + "mPr@70": 30.61, + "mPr@80": 28.2, + "mPr@90": 22.46, + "mIoU": 19.57 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..6383a50a5d --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/config.py @@ -0,0 +1,550 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS', + output_dir= + 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + save_dir= + 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/20241010_134616/20241010_134616.json b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/20241010_134616/20241010_134616.json new file mode 100644 index 0000000000..b05ff26305 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/20241010_134616/20241010_134616.json @@ -0,0 +1 @@ +{"mPr@50": 23.04, "mPr@60": 19.03, "mPr@70": 16.93, "mPr@80": 12.49, "mPr@90": 7.88, "mIoU": 16.75, "data_time": 1.0261618835585458, "time": 1.327040848277864} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..1e4f45c90a --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4352 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.0006077999588928762 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 0.01808101510366359 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.0011907917561982883 + }, + { + "gt_label": "_background_", + "pred_label": "can", + "score": 0.004138828291508634 + }, + { + "gt_label": "_background_", + "pred_label": "cap", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "cereal", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "cup", + "score": 0.0010212693186839486 + }, + { + "gt_label": "_background_", + "pred_label": "flashlight", + "score": 0.0 + }, + { + "gt_label": "_background_", + 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+++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_irl_vision_cat_hots_irl_vision_cat.py @@ -0,0 +1,332 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS', + output_dir= + 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + save_dir= + 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_label_results.json b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_label_results.json new file mode 100644 index 0000000000..58b7af6230 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_label_results.json @@ -0,0 +1,242 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 97.62, + "IoU": 96.48 + }, + "apple": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 5.31 + }, + "banana": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 19.45 + }, + "book": { + "Pr@50": 62.07, + "Pr@60": 51.72, + "Pr@70": 41.38, + "Pr@80": 27.59, + "Pr@90": 6.9, + "IoU": 47.63 + }, + "bowl": { + "Pr@50": 6.25, + "Pr@60": 6.25, + "Pr@70": 3.12, + "Pr@80": 3.12, + "Pr@90": 0.0, + "IoU": 11.91 + }, + "can": { + "Pr@50": 38.1, + "Pr@60": 28.57, + "Pr@70": 19.05, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 16.03 + }, + "cap": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cereal": { + "Pr@50": 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"Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 17.28 + }, + "plate": { + "Pr@50": 61.54, + "Pr@60": 61.54, + "Pr@70": 46.15, + "Pr@80": 46.15, + "Pr@90": 38.46, + "IoU": 56.02 + }, + "pringles": { + "Pr@50": 20.0, + "Pr@60": 10.0, + "Pr@70": 10.0, + "Pr@80": 10.0, + "Pr@90": 10.0, + "IoU": 22.38 + }, + "scissors": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "sponge": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 3.93 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_results.json b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_results.json new file mode 100644 index 0000000000..889af05587 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 23.04, + "mPr@60": 19.03, + "mPr@70": 16.93, + "mPr@80": 12.49, + "mPr@90": 7.88, + "mIoU": 16.75 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..f00ba1f812 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/show/vis_data/config.py @@ -0,0 +1,332 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS', + output_dir= + 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + save_dir= + 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/20241010_135020/20241010_135020.json b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/20241010_135020/20241010_135020.json new file mode 100644 index 0000000000..6ebad2b2b8 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/20241010_135020/20241010_135020.json @@ -0,0 +1 @@ +{"mPr@50": 36.89, "mPr@60": 30.37, "mPr@70": 24.11, "mPr@80": 18.96, "mPr@90": 9.83, "mIoU": 21.05, "data_time": 1.016529298964001, "time": 1.321800444807325} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..e559f8bf59 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4352 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.0004837589109321699 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.0008682852247500485 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 0.04676749608165619 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.0004217385377357379 + }, + { + "gt_label": "_background_", + "pred_label": "can", + "score": 0.0034855449736394812 + }, + { + "gt_label": "_background_", + "pred_label": "cap", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "cereal", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "cup", + "score": 0.0013809869765072202 + }, + { + "gt_label": "_background_", + "pred_label": "flashlight", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "fork", + "score": 1.653876618571521e-05 + }, + { + "gt_label": "_background_", + "pred_label": "juice_box", + "score": 0.035992489911662734 + }, + { + "gt_label": "_background_", + "pred_label": "keyboard", + "score": 0.002480814927857282 + }, + { + "gt_label": "_background_", + "pred_label": "knife", + "score": 9.509790556786247e-05 + }, + { + "gt_label": "_background_", + "pred_label": "laptop", + "score": 0.00017365704495000972 + }, + { + "gt_label": "_background_", + "pred_label": "lemon", + "score": 0.0 + }, + { + "gt_label": "_background_", + 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b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_irl_vision_cat_hots_irl_vision_cat.py @@ -0,0 +1,354 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=47, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS', + output_dir= + 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + save_dir= + 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_label_results.json b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_label_results.json new file mode 100644 index 0000000000..76d009ec99 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_label_results.json @@ -0,0 +1,242 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 98.96 + }, + "apple": { + "Pr@50": 60.0, + "Pr@60": 20.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 11.64 + }, + "banana": { + "Pr@50": 80.0, + "Pr@60": 80.0, + "Pr@70": 80.0, + "Pr@80": 40.0, + "Pr@90": 0.0, + "IoU": 47.7 + }, + "book": { + "Pr@50": 51.72, + "Pr@60": 48.28, + "Pr@70": 44.83, + "Pr@80": 31.03, + "Pr@90": 20.69, + "IoU": 53.2 + }, + "bowl": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 2.51 + }, + "can": { + "Pr@50": 52.38, + "Pr@60": 47.62, + "Pr@70": 19.05, + "Pr@80": 19.05, + "Pr@90": 4.76, + "IoU": 22.88 + }, + "cap": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cereal": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cup": { + "Pr@50": 59.09, + "Pr@60": 50.0, + "Pr@70": 31.82, + "Pr@80": 18.18, + "Pr@90": 0.0, + "IoU": 24.77 + }, + "flashlight": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.21 + }, + "juice_box": { + "Pr@50": 88.89, + "Pr@60": 77.78, + "Pr@70": 66.67, + "Pr@80": 55.56, + "Pr@90": 44.44, + "IoU": 45.71 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 66.67, + "IoU": 75.22 + }, + "knife": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 3.0 + }, + "laptop": { + "Pr@50": 14.29, + "Pr@60": 14.29, + "Pr@70": 14.29, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 9.41 + }, + "lemon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.97 + }, + "marker": { + "Pr@50": 14.29, + "Pr@60": 14.29, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 8.22 + }, + "milk": { + "Pr@50": 50.0, + "Pr@60": 25.0, + "Pr@70": 25.0, + "Pr@80": 25.0, + "Pr@90": 0.0, + "IoU": 4.91 + }, + "monitor": { + "Pr@50": 50.0, + "Pr@60": 25.0, + "Pr@70": 25.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 22.11 + }, + "mouse": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.15 + }, + "mug": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 75.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 0.0, + "IoU": 35.48 + }, + "peach": { + "Pr@50": 16.67, + "Pr@60": 16.67, + "Pr@70": 16.67, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 18.07 + }, + "pear": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 25.0, + "Pr@80": 25.0, + "Pr@90": 0.0, + "IoU": 13.25 + }, + "pen": { + "Pr@50": 55.56, + "Pr@60": 33.33, + "Pr@70": 11.11, + "Pr@80": 11.11, + "Pr@90": 0.0, + "IoU": 27.72 + }, + "plate": { + "Pr@50": 53.85, + "Pr@60": 53.85, + "Pr@70": 53.85, + "Pr@80": 53.85, + "Pr@90": 38.46, + "IoU": 53.48 + }, + "pringles": { + "Pr@50": 50.0, + "Pr@60": 40.0, + "Pr@70": 40.0, + "Pr@80": 20.0, + "Pr@90": 20.0, + "IoU": 20.87 + }, + "scissors": { + "Pr@50": 60.0, + "Pr@60": 40.0, + "Pr@70": 20.0, + "Pr@80": 20.0, + "Pr@90": 0.0, + "IoU": 30.28 + }, + "sponge": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.71 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_results.json b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_results.json new file mode 100644 index 0000000000..678964b1e6 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 36.89, + "mPr@60": 30.37, + "mPr@70": 24.11, + "mPr@80": 18.96, + "mPr@90": 9.83, + "mIoU": 21.05 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..4aa55fe555 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/config.py @@ -0,0 +1,354 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=47, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS', + output_dir= + 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS', + save_dir= + 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20241010_014121/20241010_014121.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20241010_014121/20241010_014121.json new file mode 100644 index 0000000000..4ee6356e3b --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20241010_014121/20241010_014121.json @@ -0,0 +1 @@ +{"mPr@50": 17.86, "mPr@60": 13.96, "mPr@70": 9.7, "mPr@80": 6.47, "mPr@90": 2.78, "mIoU": 13.53, "data_time": 0.9837384621302286, "time": 1.2368028504507882} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_irl_vision_cat_hots_cat_irl_vision_cat.py b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_irl_vision_cat_hots_cat_irl_vision_cat.py new file mode 100644 index 0000000000..f5254673a5 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_irl_vision_cat_hots_cat_irl_vision_cat.py @@ -0,0 +1,348 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS_CAT', + output_dir= + 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS_CAT', + save_dir= + 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_label_results.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_label_results.json new file mode 100644 index 0000000000..8860f5dd62 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_label_results.json @@ -0,0 +1,242 @@ +{ + "_background_": { + "Pr@50": 85.71, + "Pr@60": 25.0, + "Pr@70": 3.57, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 56.23 + }, + "apple": { + "Pr@50": 60.0, + "Pr@60": 60.0, + "Pr@70": 20.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 37.44 + }, + "banana": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "book": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 1.6 + }, + "bowl": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 1.64 + }, + "can": { + "Pr@50": 38.1, + "Pr@60": 28.57, + "Pr@70": 23.81, + "Pr@80": 4.76, + "Pr@90": 4.76, + "IoU": 36.01 + }, + "cap": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cereal": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cup": { + "Pr@50": 4.55, + "Pr@60": 4.55, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 5.49 + }, + "flashlight": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 2.94 + }, + "juice_box": { + "Pr@50": 88.89, + "Pr@60": 88.89, + "Pr@70": 77.78, + "Pr@80": 55.56, + "Pr@90": 22.22, + "IoU": 39.14 + }, + "keyboard": { + "Pr@50": 66.67, + "Pr@60": 66.67, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 33.33, + "IoU": 67.79 + }, + "knife": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "laptop": { + "Pr@50": 28.57, + "Pr@60": 14.29, + "Pr@70": 14.29, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 10.47 + }, + "lemon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 2.76 + }, + "marker": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.16 + }, + "milk": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "monitor": { + "Pr@50": 25.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 13.14 + }, + "mouse": { + "Pr@50": 12.5, + "Pr@60": 12.5, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 1.89 + }, + "mug": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "orange": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 16.68 + }, + "peach": { + "Pr@50": 16.67, + "Pr@60": 16.67, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 13.65 + }, + "pear": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pen": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 3.1 + }, + "plate": { + "Pr@50": 69.23, + "Pr@60": 61.54, + "Pr@70": 61.54, + "Pr@80": 53.85, + "Pr@90": 23.08, + "IoU": 58.72 + }, + "pringles": { + "Pr@50": 40.0, + "Pr@60": 40.0, + "Pr@70": 40.0, + "Pr@80": 30.0, + "Pr@90": 0.0, + "IoU": 37.01 + }, + "scissors": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.08 + }, + "sponge": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_results.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_results.json new file mode 100644 index 0000000000..2046f1c532 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 17.86, + "mPr@60": 13.96, + "mPr@70": 9.7, + "mPr@80": 6.47, + "mPr@90": 2.78, + "mIoU": 13.53 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..435f558e13 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4352 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.00026462035744256337 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.00022327342659216281 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 43.46941352520955 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.00012404079255120157 + }, + { + "gt_label": "_background_", + "pred_label": "can", + "score": 0.0028570729217626757 + }, + { + "gt_label": "_background_", + "pred_label": "cap", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "cereal", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "cup", + "score": 0.0005375101010552068 + }, + { + "gt_label": "_background_", + "pred_label": "flashlight", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "fork", + "score": 1.653877234016021e-05 + }, + { + "gt_label": "_background_", + "pred_label": "juice_box", + "score": 0.034838923934547475 + }, + { + "gt_label": "_background_", + "pred_label": "keyboard", + "score": 0.0028033219116571553 + }, + { + "gt_label": "_background_", + "pred_label": "knife", + "score": 0.0001529836441464819 + }, + { + "gt_label": "_background_", + "pred_label": "laptop", + "score": 0.0024973546233641912 + }, + { + "gt_label": "_background_", + "pred_label": "lemon", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "marker", + "score": 0.00045068154626936566 + }, + { + "gt_label": "_background_", + "pred_label": "milk", + "score": 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{ + "gt_label": "spoon", + "pred_label": "sponge", + "score": 0.0 + } +] \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/confusion_matrix/top_10_confusion.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..3da789f680 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "milk", + "pred_label": "book", + "score": 100.0 + }, + { + "gt_label": "sponge", + "pred_label": "book", + "score": 88.51202884669559 + }, + { + "gt_label": "cup", + "pred_label": "book", + "score": 86.9969556063796 + }, + { + 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0000000000..f5254673a5 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/show/vis_data/config.py @@ -0,0 +1,348 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS_CAT', + output_dir= + 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS_CAT', + save_dir= + 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20241010_014526/20241010_014526.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20241010_014526/20241010_014526.json new file mode 100644 index 0000000000..bcd58f3cf8 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20241010_014526/20241010_014526.json @@ -0,0 +1 @@ +{"mPr@50": 38.32, "mPr@60": 37.13, "mPr@70": 35.96, "mPr@80": 33.44, "mPr@90": 28.87, "mIoU": 22.1, "data_time": 0.9870406020255316, "time": 1.3044300845691137} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..d39fed722c --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4352 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + 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a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/top_10_confusion.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..ffb9255df3 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "lemon", + "pred_label": "pear", + "score": 92.58630187293426 + }, + { + "gt_label": "orange", + "pred_label": "apple", + "score": 91.88194570960103 + }, + { + "gt_label": "peach", + "pred_label": "pear", + "score": 87.37153419593345 + }, + { + "gt_label": "laptop", + "pred_label": "keyboard", + "score": 74.36567414975131 + }, + { + "gt_label": "fork", + "pred_label": "pen", + "score": 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b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_irl_vision_cat_hots_cat_irl_vision_cat.py @@ -0,0 +1,441 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS_CAT', + output_dir= + 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS_CAT', + save_dir= + 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_label_results.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_label_results.json new file mode 100644 index 0000000000..3f5c8ddece --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_label_results.json @@ -0,0 +1,242 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 99.44 + }, + "apple": { + "Pr@50": 80.0, + "Pr@60": 60.0, + "Pr@70": 60.0, + "Pr@80": 60.0, + "Pr@90": 40.0, + "IoU": 18.33 + }, + "banana": { + "Pr@50": 80.0, + "Pr@60": 80.0, + "Pr@70": 80.0, + "Pr@80": 80.0, + "Pr@90": 80.0, + "IoU": 56.56 + }, + "book": { + "Pr@50": 89.66, + "Pr@60": 89.66, + "Pr@70": 82.76, + "Pr@80": 79.31, + "Pr@90": 68.97, + "IoU": 56.26 + }, + "bowl": { + "Pr@50": 3.12, + "Pr@60": 3.12, + "Pr@70": 3.12, + "Pr@80": 3.12, + "Pr@90": 0.0, + "IoU": 3.14 + }, + "can": { + "Pr@50": 57.14, + "Pr@60": 57.14, + "Pr@70": 57.14, + "Pr@80": 57.14, + "Pr@90": 42.86, + "IoU": 47.36 + }, + "cap": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cereal": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cup": { + "Pr@50": 54.55, + "Pr@60": 50.0, + "Pr@70": 45.45, + "Pr@80": 40.91, + "Pr@90": 36.36, + "IoU": 26.76 + }, + "flashlight": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "juice_box": { + "Pr@50": 77.78, + "Pr@60": 77.78, + "Pr@70": 77.78, + "Pr@80": 77.78, + "Pr@90": 66.67, + "IoU": 61.72 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 83.33, + "IoU": 32.1 + }, + "knife": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "laptop": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "lemon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "marker": { + "Pr@50": 21.43, + "Pr@60": 21.43, + "Pr@70": 21.43, + "Pr@80": 7.14, + "Pr@90": 0.0, + "IoU": 8.73 + }, + "milk": { + "Pr@50": 75.0, + "Pr@60": 75.0, + "Pr@70": 75.0, + "Pr@80": 75.0, + "Pr@90": 75.0, + "IoU": 6.96 + }, + "monitor": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 50.0, + "IoU": 48.96 + }, + "mouse": { + "Pr@50": 12.5, + "Pr@60": 12.5, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.78 + }, + "mug": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "orange": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "peach": { + "Pr@50": 16.67, + "Pr@60": 16.67, + "Pr@70": 16.67, + "Pr@80": 16.67, + "Pr@90": 16.67, + "IoU": 4.49 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 31.83 + }, + "pen": { + "Pr@50": 77.78, + "Pr@60": 66.67, + "Pr@70": 55.56, + "Pr@80": 22.22, + "Pr@90": 0.0, + "IoU": 33.25 + }, + "plate": { + "Pr@50": 53.85, + "Pr@60": 53.85, + "Pr@70": 53.85, + "Pr@80": 53.85, + "Pr@90": 46.15, + "IoU": 47.57 + }, + "pringles": { + "Pr@50": 60.0, + "Pr@60": 60.0, + "Pr@70": 60.0, + "Pr@80": 60.0, + "Pr@90": 60.0, + "IoU": 51.14 + }, + "scissors": { + "Pr@50": 40.0, + "Pr@60": 40.0, + "Pr@70": 40.0, + "Pr@80": 20.0, + "Pr@90": 0.0, + "IoU": 27.5 + }, + "sponge": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_results.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_results.json new file mode 100644 index 0000000000..0069d13676 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 38.32, + "mPr@60": 37.13, + "mPr@70": 35.96, + "mPr@80": 33.44, + "mPr@90": 28.87, + "mIoU": 22.1 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..738416abbf --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show/vis_data/config.py @@ -0,0 +1,441 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS_CAT', + output_dir= + 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS_CAT', + save_dir= + 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20241010_014937/20241010_014937.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20241010_014937/20241010_014937.json new file mode 100644 index 0000000000..4645d8fa7f --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20241010_014937/20241010_014937.json @@ -0,0 +1 @@ +{"mPr@50": 28.76, "mPr@60": 26.39, "mPr@70": 24.96, "mPr@80": 22.74, "mPr@90": 17.59, "mIoU": 18.29, "data_time": 0.9893234003157843, "time": 1.282902717590332} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..7fef4285b9 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4352 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.0004300100721589979 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 0.2240187087459087 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 9.096366911055725e-05 + }, + { + "gt_label": "_background_", + 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+backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS_CAT', + output_dir= + 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS_CAT', + save_dir= + 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/maskformer_label_results.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/maskformer_label_results.json new file mode 100644 index 0000000000..673c254958 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/maskformer_label_results.json @@ -0,0 +1,242 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 98.8 + }, + "apple": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "banana": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "book": { + "Pr@50": 44.83, + "Pr@60": 41.38, + "Pr@70": 34.48, + "Pr@80": 24.14, + "Pr@90": 17.24, + "IoU": 32.69 + }, + "bowl": { + "Pr@50": 3.12, + "Pr@60": 3.12, + "Pr@70": 3.12, + "Pr@80": 3.12, + "Pr@90": 3.12, + "IoU": 6.65 + }, + "can": { + "Pr@50": 42.86, + "Pr@60": 42.86, + "Pr@70": 42.86, + "Pr@80": 42.86, + "Pr@90": 38.1, + "IoU": 37.13 + }, + "cap": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cereal": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cup": { + "Pr@50": 68.18, + "Pr@60": 59.09, + "Pr@70": 59.09, + "Pr@80": 54.55, + "Pr@90": 40.91, + "IoU": 43.4 + }, + "flashlight": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "fork": { + "Pr@50": 25.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 11.51 + }, + "juice_box": { + "Pr@50": 77.78, + "Pr@60": 55.56, + "Pr@70": 44.44, + "Pr@80": 22.22, + "Pr@90": 22.22, + "IoU": 35.71 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 52.13 + }, + "knife": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "laptop": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "lemon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "marker": { + "Pr@50": 7.14, + "Pr@60": 7.14, + "Pr@70": 7.14, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 4.97 + }, + "milk": { + "Pr@50": 75.0, + "Pr@60": 75.0, + "Pr@70": 75.0, + "Pr@80": 75.0, + "Pr@90": 25.0, + "IoU": 46.7 + }, + "monitor": { + "Pr@50": 75.0, + "Pr@60": 75.0, + "Pr@70": 75.0, + "Pr@80": 75.0, + "Pr@90": 75.0, + "IoU": 39.87 + }, + "mouse": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "mug": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 75.0, + "Pr@80": 75.0, + "Pr@90": 50.0, + "IoU": 29.8 + }, + "peach": { + "Pr@50": 16.67, + "Pr@60": 16.67, + "Pr@70": 16.67, + "Pr@80": 16.67, + "Pr@90": 0.0, + "IoU": 4.77 + }, + "pear": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pen": { + "Pr@50": 55.56, + "Pr@60": 44.44, + "Pr@70": 44.44, + "Pr@80": 22.22, + "Pr@90": 0.0, + "IoU": 19.17 + }, + "plate": { + "Pr@50": 61.54, + "Pr@60": 61.54, + "Pr@70": 61.54, + "Pr@80": 61.54, + "Pr@90": 46.15, + "IoU": 62.91 + }, + "pringles": { + "Pr@50": 10.0, + "Pr@60": 10.0, + "Pr@70": 10.0, + "Pr@80": 10.0, + "Pr@90": 10.0, + "IoU": 22.45 + }, + "scissors": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "sponge": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/maskformer_results.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/maskformer_results.json new file mode 100644 index 0000000000..a9e7689d6c --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/maskformer_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 28.76, + "mPr@60": 26.39, + "mPr@70": 24.96, + "mPr@80": 22.74, + "mPr@90": 17.59, + "mIoU": 18.29 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..b6dd329f0f --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show/vis_data/config.py @@ -0,0 +1,531 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS_CAT', + output_dir= + 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS_CAT', + save_dir= + 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20241010_015343/20241010_015343.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20241010_015343/20241010_015343.json new file mode 100644 index 0000000000..010c921655 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20241010_015343/20241010_015343.json @@ -0,0 +1 @@ +{"mPr@50": 35.63, "mPr@60": 26.28, "mPr@70": 20.38, "mPr@80": 12.73, "mPr@90": 6.94, "mIoU": 22.18, "data_time": 0.9925861614091056, "time": 1.2659318134898232} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..18462e95c9 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4352 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.001571185061370902 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.0006326087220782842 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 1.612383187559059 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.006582438467638094 + }, + { + "gt_label": "_background_", + "pred_label": "can", + "score": 0.0072109124921864555 + }, + { + "gt_label": "_background_", + "pred_label": "cap", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "cereal", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "cup", + "score": 0.00447787742490707 + }, + { + "gt_label": "_background_", + "pred_label": "flashlight", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "fork", + "score": 4.1346975299234264e-05 + }, + { + "gt_label": "_background_", + "pred_label": "juice_box", + "score": 0.15276880433561074 + }, + { + "gt_label": "_background_", + "pred_label": "keyboard", + "score": 0.002637937024091146 + }, + { + "gt_label": "_background_", + "pred_label": "knife", + "score": 0.0003721227776931084 + }, + { + "gt_label": "_background_", + "pred_label": "laptop", + "score": 1.067074469127578 + }, + { + "gt_label": "_background_", + "pred_label": "lemon", + "score": 0.0002852941295647164 + }, + { + "gt_label": "_background_", + "pred_label": "marker", + "score": 0.0015794544564307488 + }, + { + "gt_label": "_background_", + "pred_label": "milk", + "score": 0.008240452177137389 + }, + { + "gt_label": "_background_", + "pred_label": "monitor", + "score": 0.1150396893750595 + }, + { + "gt_label": "_background_", + "pred_label": "mouse", + "score": 0.00023981245673555872 + }, + { + "gt_label": "_background_", + "pred_label": "mug", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "orange", + "score": 0.0020756181600215602 + }, + { + "gt_label": "_background_", + "pred_label": "peach", + "score": 0.000942711036822541 + }, + { + "gt_label": "_background_", + "pred_label": "pear", + "score": 0.000669820999847595 + }, + { + "gt_label": "_background_", + "pred_label": "pen", + "score": 0.0005912617467790499 + }, + { + "gt_label": "_background_", + "pred_label": "plate", + "score": 0.0090012365226433 + }, + { + "gt_label": "_background_", + "pred_label": "pringles", + "score": 0.0011866581910880234 + }, + { + "gt_label": "_background_", + "pred_label": "scissors", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "sponge", + "score": 0.0 + }, + { + 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b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..7b69ee08b9 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "laptop", + "pred_label": "monitor", + "score": 56.277376558519634 + }, + { + "gt_label": "mug", + "pred_label": "juice_box", + "score": 50.117928338341244 + }, + { + "gt_label": "fork", + "pred_label": "pen", + "score": 42.843113184384976 + }, + { + "gt_label": "apple", + "pred_label": "orange", + "score": 42.16673154996941 + }, + { + "gt_label": "knife", + "pred_label": "pen", + "score": 41.79456906729634 + }, + { + "gt_label": "spoon", + "pred_label": "pen", + "score": 37.44713571703191 + }, + { + "gt_label": "mouse", + "pred_label": "spoon", + "score": 35.58490566037736 + }, + 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+crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS_CAT', + output_dir= + 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS_CAT', + save_dir= + 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/segformer_label_results.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/segformer_label_results.json new file mode 100644 index 0000000000..1c99a1b3bd --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/segformer_label_results.json @@ -0,0 +1,242 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 97.62, + "IoU": 96.18 + }, + "apple": { + "Pr@50": 40.0, + "Pr@60": 40.0, + "Pr@70": 40.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 13.15 + }, + "banana": { + "Pr@50": 80.0, + "Pr@60": 60.0, + "Pr@70": 40.0, + "Pr@80": 40.0, + "Pr@90": 0.0, + "IoU": 71.52 + }, + "book": { + "Pr@50": 37.93, + "Pr@60": 17.24, + "Pr@70": 6.9, + "Pr@80": 3.45, + "Pr@90": 0.0, + "IoU": 19.72 + }, + "bowl": { + "Pr@50": 9.38, + "Pr@60": 6.25, + "Pr@70": 6.25, + "Pr@80": 3.12, + "Pr@90": 0.0, + "IoU": 11.98 + }, + "can": { + "Pr@50": 66.67, + "Pr@60": 47.62, + "Pr@70": 42.86, + "Pr@80": 23.81, + "Pr@90": 0.0, + "IoU": 32.82 + }, + "cap": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cereal": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cup": { + "Pr@50": 40.91, + "Pr@60": 36.36, + "Pr@70": 27.27, + "Pr@80": 13.64, + "Pr@90": 0.0, + "IoU": 34.85 + }, + "flashlight": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "juice_box": { + "Pr@50": 88.89, + "Pr@60": 66.67, + "Pr@70": 55.56, + "Pr@80": 33.33, + "Pr@90": 22.22, + "IoU": 33.47 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 83.33, + "Pr@90": 50.0, + "IoU": 59.93 + }, + "knife": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "laptop": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 2.54 + }, + "lemon": { + "Pr@50": 60.0, + "Pr@60": 20.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 35.45 + }, + "marker": { + "Pr@50": 7.14, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 8.11 + }, + "milk": { + "Pr@50": 75.0, + "Pr@60": 75.0, + "Pr@70": 75.0, + "Pr@80": 25.0, + "Pr@90": 0.0, + "IoU": 8.8 + }, + "monitor": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 23.88 + }, + "mouse": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.51 + }, + "mug": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "orange": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 25.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 24.68 + }, + "peach": { + "Pr@50": 83.33, + "Pr@60": 16.67, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 37.16 + }, + "pear": { + "Pr@50": 25.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 25.38 + }, + "pen": { + "Pr@50": 55.56, + "Pr@60": 11.11, + "Pr@70": 11.11, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 24.87 + }, + "plate": { + "Pr@50": 69.23, + "Pr@60": 61.54, + "Pr@70": 61.54, + "Pr@80": 46.15, + "Pr@90": 38.46, + "IoU": 69.49 + }, + "pringles": { + "Pr@50": 30.0, + "Pr@60": 30.0, + "Pr@70": 20.0, + "Pr@80": 10.0, + "Pr@90": 0.0, + "IoU": 21.57 + }, + "scissors": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 3.19 + }, + "sponge": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 6.2 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/segformer_results.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/segformer_results.json new file mode 100644 index 0000000000..08eb6a765d --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/segformer_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 35.63, + "mPr@60": 26.28, + "mPr@70": 20.38, + "mPr@80": 12.73, + "mPr@90": 6.94, + "mIoU": 22.18 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..6fb8415202 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/show/vis_data/config.py @@ -0,0 +1,334 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS_CAT', + output_dir= + 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS_CAT', + save_dir= + 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20241010_015745/20241010_015745.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20241010_015745/20241010_015745.json new file mode 100644 index 0000000000..921d0d8933 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20241010_015745/20241010_015745.json @@ -0,0 +1 @@ +{"mPr@50": 31.37, "mPr@60": 27.33, "mPr@70": 21.99, "mPr@80": 18.25, "mPr@90": 10.01, "mIoU": 20.06, "data_time": 0.9858090224720183, "time": 1.2510920422417777} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..c35af9f05c --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4352 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.0004713546414022784 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.0006615503738979345 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 0.23211735275160095 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.0004258730531967954 + }, + { + "gt_label": "_background_", + "pred_label": "can", 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"pred_label": "pringles", + "score": 0.0 + }, + { + "gt_label": "spoon", + "pred_label": "scissors", + "score": 4.393939393939394 + }, + { + "gt_label": "spoon", + "pred_label": "sponge", + "score": 0.0 + } +] \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/top_10_confusion.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..9093f5a02e --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "peach", + "pred_label": "pear", + "score": 79.57855822550832 + }, + { + "gt_label": "milk", + "pred_label": "book", + "score": 73.14504547630445 + }, + { + "gt_label": "knife", + "pred_label": "pen", + "score": 70.46217402530338 + }, + { + "gt_label": "lemon", + "pred_label": "pear", + "score": 69.0093646713184 + }, + { + "gt_label": "laptop", + "pred_label": "monitor", + "score": 57.706397351661096 + }, + { + "gt_label": "cereal", + "pred_label": "book", + "score": 55.196698895194764 + }, + { + "gt_label": "marker", + "pred_label": "pen", + "score": 40.3980278980279 + }, + { + "gt_label": "mouse", + "pred_label": "spoon", + "score": 39.57649376231123 + }, + { + "gt_label": "apple", + "pred_label": "orange", + "score": 38.76313886880596 + }, + { + "gt_label": "sponge", + "pred_label": "juice_box", + "score": 36.328807256746856 + } +] \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_irl_vision_cat_hots_cat_irl_vision_cat.py b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_irl_vision_cat_hots_cat_irl_vision_cat.py new file mode 100644 index 0000000000..3ebb7c5481 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_irl_vision_cat_hots_cat_irl_vision_cat.py @@ -0,0 +1,356 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=26, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS_CAT', + output_dir= + 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS_CAT', + save_dir= + 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_label_results.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_label_results.json new file mode 100644 index 0000000000..c2c363dd36 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_label_results.json @@ -0,0 +1,242 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 99.01 + }, + "apple": { + "Pr@50": 40.0, + "Pr@60": 20.0, + "Pr@70": 20.0, + "Pr@80": 20.0, + "Pr@90": 0.0, + "IoU": 20.15 + }, + "banana": { + "Pr@50": 100.0, + "Pr@60": 80.0, + "Pr@70": 60.0, + "Pr@80": 20.0, + "Pr@90": 0.0, + "IoU": 40.7 + }, + "book": { + "Pr@50": 75.86, + "Pr@60": 62.07, + "Pr@70": 44.83, + "Pr@80": 44.83, + "Pr@90": 13.79, + "IoU": 50.62 + }, + "bowl": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 1.86 + }, + "can": { + "Pr@50": 66.67, + "Pr@60": 47.62, + "Pr@70": 47.62, + "Pr@80": 33.33, + "Pr@90": 23.81, + "IoU": 51.19 + }, + "cap": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cereal": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cup": { + "Pr@50": 54.55, + "Pr@60": 45.45, + "Pr@70": 31.82, + "Pr@80": 22.73, + "Pr@90": 9.09, + "IoU": 27.9 + }, + "flashlight": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 5.55 + }, + "juice_box": { + "Pr@50": 88.89, + "Pr@60": 77.78, + "Pr@70": 66.67, + "Pr@80": 44.44, + "Pr@90": 11.11, + "IoU": 39.43 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 66.67, + "IoU": 46.53 + }, + "knife": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "laptop": { + "Pr@50": 14.29, + "Pr@60": 14.29, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 8.14 + }, + "lemon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 9.15 + }, + "marker": { + "Pr@50": 7.14, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 2.79 + }, + "milk": { + "Pr@50": 25.0, + "Pr@60": 25.0, + "Pr@70": 25.0, + "Pr@80": 25.0, + "Pr@90": 0.0, + "IoU": 10.11 + }, + "monitor": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 25.0, + "IoU": 40.14 + }, + "mouse": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.17 + }, + "mug": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "orange": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 4.89 + }, + "peach": { + "Pr@50": 16.67, + "Pr@60": 16.67, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 3.86 + }, + "pear": { + "Pr@50": 25.0, + "Pr@60": 25.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 14.27 + }, + "pen": { + "Pr@50": 55.56, + "Pr@60": 44.44, + "Pr@70": 22.22, + "Pr@80": 11.11, + "Pr@90": 0.0, + "IoU": 24.0 + }, + "plate": { + "Pr@50": 61.54, + "Pr@60": 61.54, + "Pr@70": 61.54, + "Pr@80": 46.15, + "Pr@90": 30.77, + "IoU": 53.05 + }, + "pringles": { + "Pr@50": 40.0, + "Pr@60": 30.0, + "Pr@70": 30.0, + "Pr@80": 30.0, + "Pr@90": 20.0, + "IoU": 24.27 + }, + "scissors": { + "Pr@50": 20.0, + "Pr@60": 20.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 24.05 + }, + "sponge": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_results.json b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_results.json new file mode 100644 index 0000000000..3e90c90383 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 31.37, + "mPr@60": 27.33, + "mPr@70": 21.99, + "mPr@80": 18.25, + "mPr@90": 10.01, + "mIoU": 20.06 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..3ebb7c5481 --- /dev/null +++ b/my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show/vis_data/config.py @@ -0,0 +1,356 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=26, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='HOTS_CAT', + output_dir= + 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='HOTS_CAT', + save_dir= + 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/20241010_132749/20241010_132749.json b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/20241010_132749/20241010_132749.json new file mode 100644 index 0000000000..938742fe95 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/20241010_132749/20241010_132749.json @@ -0,0 +1 @@ +{"mPr@50": 19.07, "mPr@60": 14.02, "mPr@70": 11.55, "mPr@80": 8.05, "mPr@90": 4.25, "mIoU": 22.2, "data_time": 0.9879734796636245, "time": 1.4076155494241154} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_hots_cat_irl_vision_hots_cat.py b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_hots_cat_irl_vision_hots_cat.py new file mode 100644 index 0000000000..64da22e435 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_hots_cat_irl_vision_hots_cat.py @@ -0,0 +1,348 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_label_results.json b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_label_results.json new file mode 100644 index 0000000000..77ea608bde --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_label_results.json @@ -0,0 +1,210 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 70.59, + "IoU": 91.27 + }, + "apple": { + "Pr@50": 33.33, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 30.2 + }, + "banana": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 33.33, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 30.56 + }, + "book": { + "Pr@50": 50.0, + "Pr@60": 37.5, + "Pr@70": 37.5, + "Pr@80": 25.0, + "Pr@90": 0.0, + "IoU": 44.3 + }, + "bowl": { + "Pr@50": 16.67, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 11.03 + }, + "can": { + "Pr@50": 20.0, + "Pr@60": 20.0, + "Pr@70": 10.0, + "Pr@80": 10.0, + "Pr@90": 0.0, + "IoU": 27.54 + }, + "cup": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 3.64 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "juice_box": { + "Pr@50": 85.71, + "Pr@60": 57.14, + "Pr@70": 42.86, + "Pr@80": 14.29, + "Pr@90": 0.0, + "IoU": 57.68 + }, + "keyboard": { + "Pr@50": 80.0, + "Pr@60": 80.0, + "Pr@70": 60.0, + "Pr@80": 60.0, + 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a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_results.json b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_results.json new file mode 100644 index 0000000000..c2b08d8b90 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 19.07, + "mPr@60": 14.02, + "mPr@70": 11.55, + "mPr@80": 8.05, + "mPr@90": 4.25, + "mIoU": 22.2 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json 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b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "pear", + "pred_label": "peach", + "score": 90.03871876099964 + }, + { + "gt_label": "monitor", + "pred_label": "laptop", + "score": 82.52537689521382 + }, + { + "gt_label": "orange", + "pred_label": "apple", + "score": 67.68781911013858 + }, + { + "gt_label": "milk", + "pred_label": "book", + "score": 56.859733801374865 + }, + { + "gt_label": "stapler", + "pred_label": "book", + "score": 55.30350766645663 + }, + { + "gt_label": "knife", + "pred_label": "cup", + "score": 49.95559502664298 + }, + { + "gt_label": "scissors", + "pred_label": "marker", + "score": 46.99560332193454 + }, + { + "gt_label": "fork", + "pred_label": "marker", + "score": 42.788244159758854 + }, + { + "gt_label": "spoon", + "pred_label": "marker", + "score": 33.07200929152149 + }, + { + "gt_label": "lemon", + "pred_label": "peach", + "score": 33.00970873786408 + } +] \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..64da22e435 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/show/vis_data/config.py @@ -0,0 +1,348 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20241010_132856/20241010_132856.json b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20241010_132856/20241010_132856.json new file mode 100644 index 0000000000..fabb8c0060 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20241010_132856/20241010_132856.json @@ -0,0 +1 @@ +{"mPr@50": 20.79, "mPr@60": 18.9, "mPr@70": 15.88, "mPr@80": 11.81, "mPr@90": 5.74, "mIoU": 21.32, "data_time": 1.0164752567515654, "time": 1.4714890367844526} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..bf1052fa5b --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json @@ -0,0 +1,3252 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.01028436022280599 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.016899073729747114 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 0.21033854010234343 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.10497059491050387 + }, + { + "gt_label": "_background_", + "pred_label": "can", + "score": 0.04394226640653468 + }, + { + "gt_label": "_background_", + "pred_label": "cup", + "score": 0.007128931518081424 + }, + { + "gt_label": "_background_", + "pred_label": "fork", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "juice_box", + "score": 0.1721461660021957 + }, + { + "gt_label": "_background_", + "pred_label": "keyboard", + "score": 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b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_hots_cat_irl_vision_hots_cat.py @@ -0,0 +1,487 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_label_results.json b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_label_results.json new file mode 100644 index 0000000000..97e66605d7 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_label_results.json @@ -0,0 +1,210 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 88.24, + "IoU": 94.23 + }, + "apple": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "banana": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 33.33, + "Pr@80": 33.33, + "Pr@90": 0.0, + "IoU": 34.66 + }, + "book": { + "Pr@50": 50.0, + "Pr@60": 37.5, + "Pr@70": 37.5, + "Pr@80": 37.5, + "Pr@90": 12.5, + "IoU": 49.12 + }, + "bowl": { + "Pr@50": 66.67, + "Pr@60": 66.67, + "Pr@70": 66.67, + "Pr@80": 33.33, + "Pr@90": 0.0, + "IoU": 57.2 + }, + "can": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 10.3 + }, + "cup": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 7.66 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "juice_box": { + "Pr@50": 57.14, + "Pr@60": 57.14, + "Pr@70": 57.14, + "Pr@80": 42.86, + "Pr@90": 28.57, + "IoU": 54.61 + }, + "keyboard": { + "Pr@50": 40.0, + "Pr@60": 40.0, + "Pr@70": 40.0, + "Pr@80": 40.0, + "Pr@90": 20.0, + "IoU": 34.32 + }, + "knife": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.75 + }, + "laptop": { + "Pr@50": 20.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 19.62 + }, + "lemon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 27.03 + }, + "marker": { + "Pr@50": 20.0, + "Pr@60": 20.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 4.71 + }, + "milk": { + "Pr@50": 25.0, + "Pr@60": 25.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 18.04 + }, + "monitor": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "mouse": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "orange": { + "Pr@50": 25.0, + "Pr@60": 25.0, + "Pr@70": 25.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 50.33 + }, + "peach": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 33.33, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 22.86 + }, + "pear": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pen": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 8.22 + }, + "plate": { + "Pr@50": 20.0, + "Pr@60": 20.0, + "Pr@70": 20.0, + "Pr@80": 20.0, + "Pr@90": 0.0, + "IoU": 17.26 + }, + "pringles": { + "Pr@50": 50.0, + "Pr@60": 33.33, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 41.65 + }, + "scissors": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 1.73 + }, + "stapler": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_results.json b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_results.json new file mode 100644 index 0000000000..c3f21d838f --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 20.79, + "mPr@60": 18.9, + "mPr@70": 15.88, + "mPr@80": 11.81, + "mPr@90": 5.74, + "mIoU": 21.32 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..bc322afa2a --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py @@ -0,0 +1,487 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20241010_133004/20241010_133004.json b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20241010_133004/20241010_133004.json new file mode 100644 index 0000000000..fae8e960a3 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20241010_133004/20241010_133004.json @@ -0,0 +1 @@ +{"mPr@50": 18.42, "mPr@60": 15.76, "mPr@70": 11.9, "mPr@80": 10.01, "mPr@90": 5.9, "mIoU": 22.24, "data_time": 1.0147173825432272, "time": 1.4547865390777588} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..f3781bcfef --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json @@ -0,0 +1,3252 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 0.11979133938394097 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.04194689883692903 + }, + { + "gt_label": "_background_", + "pred_label": "can", + "score": 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/dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_hots_cat_irl_vision_hots_cat.py @@ -0,0 +1,577 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_label_results.json b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_label_results.json new file mode 100644 index 0000000000..bcf2633d3c --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_label_results.json @@ -0,0 +1,210 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 94.12, + "IoU": 94.75 + }, + "apple": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "banana": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "book": { + "Pr@50": 75.0, + "Pr@60": 62.5, + "Pr@70": 50.0, + "Pr@80": 37.5, + "Pr@90": 25.0, + "IoU": 59.06 + }, + "bowl": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 12.73 + }, + "can": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 21.99 + }, + "cup": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.32 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "juice_box": { + "Pr@50": 57.14, + "Pr@60": 57.14, + "Pr@70": 42.86, + "Pr@80": 42.86, + "Pr@90": 14.29, + "IoU": 63.17 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 80.0, + "Pr@90": 20.0, + "IoU": 84.03 + }, + "knife": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "laptop": { + "Pr@50": 40.0, + "Pr@60": 20.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 44.17 + }, + "lemon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "marker": { + "Pr@50": 20.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 11.28 + }, + "milk": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 11.44 + }, + "monitor": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "mouse": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 29.7 + }, + "orange": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 22.51 + }, + "peach": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 23.02 + }, + "pear": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pen": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 11.12 + }, + "plate": { + "Pr@50": 20.0, + "Pr@60": 20.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 20.31 + }, + "pringles": { + "Pr@50": 66.67, + "Pr@60": 50.0, + "Pr@70": 16.67, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 52.77 + }, + "scissors": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 15.81 + }, + "stapler": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_results.json b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_results.json new file mode 100644 index 0000000000..77c3c77a1e --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 18.42, + "mPr@60": 15.76, + "mPr@70": 11.9, + "mPr@80": 10.01, + "mPr@90": 5.9, + "mIoU": 22.24 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..6799b7d32d --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py @@ -0,0 +1,577 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/20241010_133112/20241010_133112.json b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/20241010_133112/20241010_133112.json new file mode 100644 index 0000000000..fbfa729973 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/20241010_133112/20241010_133112.json @@ -0,0 +1 @@ +{"mPr@50": 12.61, "mPr@60": 10.75, "mPr@70": 5.36, "mPr@80": 4.33, "mPr@90": 1.81, "mIoU": 16.73, "data_time": 0.9916495154885685, "time": 1.3897903526530546} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..d69019508c --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json @@ -0,0 +1,3252 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.00014395365843827555 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 0.5622110130306852 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.03869954184348974 + }, + { + "gt_label": "_background_", + "pred_label": "can", + "score": 0.2622115888453189 + }, + { + "gt_label": "_background_", + "pred_label": "cup", + "score": 0.014155443079763762 + }, + { + "gt_label": "_background_", + "pred_label": "fork", + "score": 0.008877142270360325 + }, + { + "gt_label": "_background_", + "pred_label": "juice_box", + "score": 0.28411653720434316 + }, + { + "gt_label": "_background_", + "pred_label": "keyboard", + "score": 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b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "can", + "pred_label": "pringles", + "score": 73.37823548342605 + }, + { + "gt_label": "pear", + "pred_label": "peach", + "score": 72.30657989877078 + }, + { + "gt_label": "monitor", + "pred_label": "laptop", + "score": 71.79727452347745 + }, + { + "gt_label": "milk", + "pred_label": "book", + "score": 69.9045304634612 + }, + { + "gt_label": "lemon", + "pred_label": "peach", + "score": 69.019248395967 + }, + { + "gt_label": "orange", + "pred_label": "can", + "score": 61.34597156398104 + }, + { + "gt_label": "mouse", + "pred_label": "can", + "score": 52.64010137989299 + }, + { + "gt_label": "stapler", + "pred_label": "knife", + "score": 49.27673381100446 + }, + { + "gt_label": "pen", + "pred_label": "knife", + "score": 48.01973016103293 + }, + { + "gt_label": "scissors", + "pred_label": "spoon", + "score": 46.32788026453184 + } +] \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_hots_cat_irl_vision_hots_cat.py b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_hots_cat_irl_vision_hots_cat.py new file mode 100644 index 0000000000..2c8b020e00 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_hots_cat_irl_vision_hots_cat.py @@ -0,0 +1,334 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_label_results.json b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_label_results.json new file mode 100644 index 0000000000..20e2778eaa --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_label_results.json @@ -0,0 +1,210 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 47.06, + "IoU": 89.02 + }, + "apple": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 3.36 + }, + "banana": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 17.04 + }, + 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"Pr@90": 0.0, + "IoU": 15.91 + }, + "plate": { + "Pr@50": 20.0, + "Pr@60": 20.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 17.68 + }, + "pringles": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 31.88 + }, + "scissors": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 10.44 + }, + "stapler": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_results.json b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_results.json new file mode 100644 index 0000000000..8d662d572f --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 12.61, + "mPr@60": 10.75, + "mPr@70": 5.36, + "mPr@80": 4.33, + "mPr@90": 1.81, + "mIoU": 16.73 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..2c8b020e00 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/show/vis_data/config.py @@ -0,0 +1,334 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20241010_133216/20241010_133216.json b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20241010_133216/20241010_133216.json new file mode 100644 index 0000000000..c7c0610aa6 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20241010_133216/20241010_133216.json @@ -0,0 +1 @@ +{"mPr@50": 28.85, "mPr@60": 23.36, "mPr@70": 20.85, "mPr@80": 9.24, "mPr@90": 5.42, "mIoU": 25.02, "data_time": 1.0756372002994312, "time": 1.4718198075014002} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..350e269890 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json @@ -0,0 +1,3252 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.016761700357450286 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.0003511529404493775 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 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file mode 100644 index 0000000000..58ac2ba723 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_hots_cat_irl_vision_hots_cat.py @@ -0,0 +1,356 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=72, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_label_results.json b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_label_results.json new file mode 100644 index 0000000000..dc732e5a12 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_label_results.json @@ -0,0 +1,210 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 94.12, + "IoU": 94.56 + }, + "apple": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 33.33, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 38.24 + }, + "banana": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "book": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 12.5, + "Pr@90": 12.5, + "IoU": 52.56 + }, + "bowl": { + "Pr@50": 16.67, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 21.11 + }, + "can": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 9.9 + }, + "cup": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 2.95 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 2.29 + }, + "juice_box": { + "Pr@50": 100.0, + "Pr@60": 85.71, + "Pr@70": 57.14, + "Pr@80": 42.86, + "Pr@90": 14.29, + "IoU": 65.06 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 80.0, + "Pr@80": 40.0, + "Pr@90": 20.0, + "IoU": 73.73 + }, + "knife": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 1.98 + }, + "laptop": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 29.86 + }, + "lemon": { + "Pr@50": 50.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 17.95 + }, + "marker": { + "Pr@50": 20.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 13.92 + }, + "milk": { + "Pr@50": 100.0, + "Pr@60": 75.0, + "Pr@70": 75.0, + "Pr@80": 25.0, + "Pr@90": 0.0, + "IoU": 56.92 + }, + "monitor": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 1.0 + }, + "mouse": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 1.33 + }, + "orange": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "peach": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 33.33, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 35.8 + }, + "pear": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 5.61 + }, + "pen": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 4.4 + }, + "plate": { + "Pr@50": 80.0, + "Pr@60": 80.0, + "Pr@70": 80.0, + "Pr@80": 20.0, + "Pr@90": 0.0, + "IoU": 72.34 + }, + "pringles": { + "Pr@50": 66.67, + "Pr@60": 50.0, + "Pr@70": 33.33, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 48.88 + }, + "scissors": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "stapler": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_results.json b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_results.json new file mode 100644 index 0000000000..67f6306c7e --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 28.85, + "mPr@60": 23.36, + "mPr@70": 20.85, + "mPr@80": 9.24, + "mPr@90": 5.42, + "mIoU": 25.02 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..58ac2ba723 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py @@ -0,0 +1,356 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=72, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/20241010_012122/20241010_012122.json b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/20241010_012122/20241010_012122.json new file mode 100644 index 0000000000..dc955aa425 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/20241010_012122/20241010_012122.json @@ -0,0 +1 @@ +{"mPr@50": 81.72, "mPr@60": 81.01, "mPr@70": 79.49, "mPr@80": 74.59, "mPr@90": 47.76, "mIoU": 73.66, "data_time": 0.9595985582896641, "time": 1.2101071193104698} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_irl_vision_cat_irl_vision_irl_vision_cat.py b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_irl_vision_cat_irl_vision_irl_vision_cat.py new file mode 100644 index 0000000000..c050b86b1d --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_irl_vision_cat_irl_vision_irl_vision_cat.py @@ -0,0 +1,348 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_label_results.json b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_label_results.json new file mode 100644 index 0000000000..d79ca2fdf9 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_label_results.json @@ -0,0 +1,242 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 99.41 + }, + "apple": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 20.0, + "IoU": 90.62 + }, + "banana": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 80.0, + "IoU": 91.44 + }, + "book": { + "Pr@50": 93.1, + "Pr@60": 93.1, + "Pr@70": 93.1, + "Pr@80": 93.1, + "Pr@90": 72.41, + "IoU": 88.31 + }, + "bowl": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 75.0, + "IoU": 93.45 + }, + "can": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 90.48, + "Pr@90": 47.62, + "IoU": 90.71 + }, + "cap": { + "Pr@50": 92.86, + "Pr@60": 92.86, + "Pr@70": 92.86, + "Pr@80": 92.86, + "Pr@90": 92.86, + "IoU": 89.81 + }, + "cereal": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 46.15, + "IoU": 87.8 + }, + "cup": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 95.45, + "Pr@90": 13.64, + "IoU": 86.8 + }, + "flashlight": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 93.75, + "Pr@80": 68.75, + "Pr@90": 18.75, + "IoU": 80.9 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "juice_box": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 77.78, + "IoU": 91.82 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 93.46 + }, + "knife": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "laptop": { + "Pr@50": 100.0, + "Pr@60": 85.71, + "Pr@70": 85.71, + "Pr@80": 85.71, + "Pr@90": 85.71, + "IoU": 90.76 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 60.0, + "IoU": 92.22 + }, + "marker": { + "Pr@50": 85.71, + "Pr@60": 78.57, + "Pr@70": 64.29, + "Pr@80": 57.14, + "Pr@90": 14.29, + "IoU": 52.4 + }, + "milk": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 75.0, + "Pr@80": 50.0, + "Pr@90": 0.0, + "IoU": 80.54 + }, + "monitor": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.09 + }, + "mouse": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 5.87 + }, + "mug": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 94.12, + "Pr@90": 23.53, + "IoU": 87.91 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 93.38 + }, + "peach": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 66.67, + "IoU": 93.0 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 75.0, + "IoU": 93.85 + }, + "pen": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "plate": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 92.8 + }, + "pringles": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 90.0, + "Pr@90": 30.0, + "IoU": 85.96 + }, + "scissors": { + "Pr@50": 80.0, + "Pr@60": 80.0, + "Pr@70": 80.0, + "Pr@80": 20.0, + "Pr@90": 0.0, + "IoU": 65.65 + }, + "sponge": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 33.33, + "IoU": 82.4 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 12.3 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_results.json b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_results.json new file mode 100644 index 0000000000..2723632b80 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 81.72, + "mPr@60": 81.01, + "mPr@70": 79.49, + "mPr@80": 74.59, + "mPr@90": 47.76, + "mIoU": 73.66 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..b30ffa4c77 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4352 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.0032002475519656735 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.004974028171853624 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 0.044844812595115885 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.02515543424568367 + }, + { + "gt_label": "_background_", + "pred_label": "can", + "score": 0.014938623262599456 + }, + { + "gt_label": "_background_", + "pred_label": "cap", + "score": 0.01629893520652285 + }, + { + "gt_label": "_background_", + "pred_label": "cereal", + "score": 0.016104604928819505 + }, + { + "gt_label": "_background_", + "pred_label": "cup", + "score": 0.015798638108605732 + }, + { + "gt_label": "_background_", + "pred_label": "flashlight", + "score": 0.02209990073030559 + }, + { + "gt_label": "_background_", + "pred_label": "fork", + "score": 4.1346867596455724e-06 + }, + { + "gt_label": "_background_", + "pred_label": "juice_box", + "score": 0.021347387740050093 + }, + { + "gt_label": "_background_", + "pred_label": "keyboard", + "score": 0.00762022769802679 + }, + { + "gt_label": "_background_", + "pred_label": "knife", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "laptop", + "score": 0.010791532442674947 + }, + { + "gt_label": "_background_", + "pred_label": "lemon", + "score": 0.004845852882304612 + }, + { + "gt_label": "_background_", + "pred_label": "marker", + "score": 0.004816910074987092 + }, + { + "gt_label": "_background_", + "pred_label": "milk", + "score": 0.0020094577651877483 + }, + { + "gt_label": "_background_", + "pred_label": "monitor", + "score": 0.007979945446115955 + }, + { + "gt_label": "_background_", + "pred_label": "mouse", + "score": 1.2404060278936719e-05 + }, + { + "gt_label": "_background_", + "pred_label": "mug", + "score": 0.014946892636118744 + }, + { + "gt_label": "_background_", + "pred_label": "orange", + "score": 0.005726541162109119 + }, + { + "gt_label": "_background_", + "pred_label": "peach", + "score": 0.003973433976019396 + }, + { + "gt_label": "_background_", + "pred_label": "pear", + "score": 0.00342765532374618 + }, + { + "gt_label": "_background_", + "pred_label": "pen", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "plate", + "score": 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\ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/confusion_matrix/top_10_confusion.json b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..910d2e5da6 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "knife", + "pred_label": "marker", + "score": 56.95843015750065 + }, + { + "gt_label": "fork", + "pred_label": "marker", + "score": 45.224650135035596 + }, + { + "gt_label": "spoon", + "pred_label": "marker", + "score": 39.77272727272727 + }, + { + "gt_label": "fork", + "pred_label": "spoon", 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--- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/show/vis_data/config.py @@ -0,0 +1,348 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20241010_012519/20241010_012519.json b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20241010_012519/20241010_012519.json new file mode 100644 index 0000000000..91ba3a4ce0 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20241010_012519/20241010_012519.json @@ -0,0 +1 @@ +{"mPr@50": 96.24, "mPr@60": 96.24, "mPr@70": 95.4, "mPr@80": 94.74, "mPr@90": 86.16, "mIoU": 89.61, "data_time": 0.9830198146048046, "time": 1.3152517506054469} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..6c1f47ff3d --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4352 @@ +[ + { + "gt_label": "_background_", + 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a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/top_10_confusion.json b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..a0892db8a5 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "fork", + "pred_label": "spoon", + "score": 40.535232015713234 + }, + { + "gt_label": "pringles", + "pred_label": "pen", + "score": 13.758587459976996 + }, + { + "gt_label": "laptop", + "pred_label": "book", + "score": 12.021104987229467 + }, + { + "gt_label": "marker", + "pred_label": "milk", + "score": 3.8059163059163055 + }, + { + "gt_label": "cap", + "pred_label": "plate", + "score": 3.602453291426576 + }, + { + "gt_label": "book", + "pred_label": "cereal", + "score": 3.0768984863257836 + }, + { + "gt_label": "book", + "pred_label": "plate", + "score": 2.602177005578377 + }, + { + "gt_label": "flashlight", + "pred_label": "marker", + "score": 2.100140863106672 + }, + { + "gt_label": "bowl", + "pred_label": "mug", + "score": 0.036793604602545445 + }, + { + "gt_label": "marker", + "pred_label": "apple", + "score": 0.03607503607503607 + } +] \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_irl_vision_cat_irl_vision_irl_vision_cat.py b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_irl_vision_cat_irl_vision_irl_vision_cat.py new file mode 100644 index 0000000000..beef548a9a --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_irl_vision_cat_irl_vision_irl_vision_cat.py @@ -0,0 +1,487 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_label_results.json b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_label_results.json new file mode 100644 index 0000000000..7549a02ba6 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_label_results.json @@ -0,0 +1,242 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 99.77 + }, + "apple": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.39 + }, + "banana": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.66 + }, + "book": { + "Pr@50": 89.66, + "Pr@60": 89.66, + "Pr@70": 89.66, + "Pr@80": 89.66, + "Pr@90": 89.66, + "IoU": 82.66 + }, + "bowl": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.38 + }, + "can": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.83 + }, + "cap": { + "Pr@50": 92.86, + "Pr@60": 92.86, + "Pr@70": 92.86, + "Pr@80": 92.86, + "Pr@90": 92.86, + "IoU": 94.03 + }, + "cereal": { + "Pr@50": 92.31, + "Pr@60": 92.31, + "Pr@70": 92.31, + "Pr@80": 92.31, + "Pr@90": 92.31, + "IoU": 91.26 + }, + "cup": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.42 + }, + "flashlight": { + "Pr@50": 93.75, + "Pr@60": 93.75, + "Pr@70": 93.75, + "Pr@80": 93.75, + "Pr@90": 93.75, + "IoU": 94.65 + }, + "fork": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 50.0, + "IoU": 52.77 + }, + "juice_box": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 88.89, + "IoU": 95.1 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.49 + }, + "knife": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 40.0, + "IoU": 89.51 + }, + "laptop": { + "Pr@50": 85.71, + "Pr@60": 85.71, + "Pr@70": 85.71, + "Pr@80": 85.71, + "Pr@90": 85.71, + "IoU": 86.04 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 80.0, + "Pr@90": 80.0, + "IoU": 94.07 + }, + "marker": { + "Pr@50": 92.86, + "Pr@60": 92.86, + "Pr@70": 92.86, + "Pr@80": 92.86, + "Pr@90": 85.71, + "IoU": 83.62 + }, + "milk": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 75.0, + "Pr@80": 75.0, + "Pr@90": 75.0, + "IoU": 85.57 + }, + "monitor": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 98.56 + }, + "mouse": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 37.5, + "IoU": 88.75 + }, + "mug": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.77 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.59 + }, + "peach": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.09 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.87 + }, + "pen": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 33.33, + "IoU": 59.12 + }, + "plate": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 92.53 + }, + "pringles": { + "Pr@50": 90.0, + "Pr@60": 90.0, + "Pr@70": 90.0, + "Pr@80": 90.0, + "Pr@90": 90.0, + "IoU": 82.84 + }, + "scissors": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.33 + }, + "sponge": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.32 + }, + "spoon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 50.0, + "IoU": 55.25 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_results.json b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_results.json new file mode 100644 index 0000000000..7a993aacc0 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 96.24, + "mPr@60": 96.24, + "mPr@70": 95.4, + "mPr@80": 94.74, + "mPr@90": 86.16, + "mIoU": 89.61 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..beef548a9a --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py @@ -0,0 +1,487 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20241010_012926/20241010_012926.json b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20241010_012926/20241010_012926.json new file mode 100644 index 0000000000..eb0bb24ee4 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20241010_012926/20241010_012926.json @@ -0,0 +1 @@ +{"mPr@50": 91.36, "mPr@60": 90.31, "mPr@70": 88.03, "mPr@80": 86.86, "mPr@90": 78.26, "mIoU": 85.71, "data_time": 0.9802745126542591, "time": 1.2761864747319902} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..7602141e39 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4352 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.0006284723874661271 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.0019515721505527106 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 0.03496704592632261 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.01092797710574325 + }, + { + "gt_label": "_background_", + "pred_label": "can", + "score": 0.004713542905995953 + }, + { + "gt_label": "_background_", + "pred_label": "cap", + "score": 0.005693463668031954 + 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b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_irl_vision_cat_irl_vision_irl_vision_cat.py @@ -0,0 +1,577 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_label_results.json b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_label_results.json new file mode 100644 index 0000000000..c2915fce88 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_label_results.json @@ -0,0 +1,242 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 99.75 + }, + "apple": { + "Pr@50": 80.0, + "Pr@60": 80.0, + "Pr@70": 80.0, + "Pr@80": 80.0, + "Pr@90": 80.0, + "IoU": 71.81 + }, + "banana": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.47 + }, + "book": { + "Pr@50": 93.1, + "Pr@60": 93.1, + "Pr@70": 93.1, + "Pr@80": 93.1, + "Pr@90": 93.1, + "IoU": 92.67 + }, + "bowl": { + "Pr@50": 96.88, + "Pr@60": 96.88, + "Pr@70": 96.88, + "Pr@80": 93.75, + "Pr@90": 93.75, + "IoU": 94.75 + }, + "can": { + "Pr@50": 95.24, + "Pr@60": 95.24, + "Pr@70": 90.48, + "Pr@80": 85.71, + "Pr@90": 71.43, + "IoU": 86.24 + }, + "cap": { + "Pr@50": 85.71, + "Pr@60": 85.71, + "Pr@70": 85.71, + "Pr@80": 85.71, + "Pr@90": 78.57, + "IoU": 87.88 + }, + "cereal": { + "Pr@50": 100.0, + "Pr@60": 92.31, + "Pr@70": 92.31, + "Pr@80": 92.31, + "Pr@90": 92.31, + "IoU": 88.52 + }, + "cup": { + "Pr@50": 90.91, + "Pr@60": 90.91, + "Pr@70": 90.91, + "Pr@80": 90.91, + "Pr@90": 90.91, + "IoU": 87.37 + }, + "flashlight": { + "Pr@50": 93.75, + "Pr@60": 93.75, + "Pr@70": 93.75, + "Pr@80": 93.75, + "Pr@90": 87.5, + "IoU": 93.07 + }, + "fork": { + "Pr@50": 75.0, + "Pr@60": 75.0, + "Pr@70": 75.0, + "Pr@80": 75.0, + "Pr@90": 50.0, + "IoU": 77.37 + }, + "juice_box": { + "Pr@50": 77.78, + "Pr@60": 77.78, + "Pr@70": 77.78, + "Pr@80": 77.78, + "Pr@90": 66.67, + "IoU": 79.06 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.73 + }, + "knife": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 80.0, + "IoU": 67.59 + }, + "laptop": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 85.71, + "Pr@80": 85.71, + "Pr@90": 85.71, + "IoU": 93.92 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 80.0, + "Pr@90": 80.0, + "IoU": 94.84 + }, + "marker": { + "Pr@50": 71.43, + "Pr@60": 64.29, + "Pr@70": 64.29, + "Pr@80": 57.14, + "Pr@90": 50.0, + "IoU": 64.67 + }, + "milk": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.06 + }, + "monitor": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 98.61 + }, + "mouse": { + "Pr@50": 75.0, + "Pr@60": 75.0, + "Pr@70": 75.0, + "Pr@80": 75.0, + "Pr@90": 25.0, + "IoU": 74.05 + }, + "mug": { + "Pr@50": 88.24, + "Pr@60": 88.24, + "Pr@70": 88.24, + "Pr@80": 88.24, + "Pr@90": 88.24, + "IoU": 91.25 + }, + "orange": { + "Pr@50": 75.0, + "Pr@60": 75.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 50.0, + "IoU": 76.96 + }, + "peach": { + "Pr@50": 100.0, + "Pr@60": 83.33, + "Pr@70": 66.67, + "Pr@80": 66.67, + "Pr@90": 66.67, + "IoU": 77.19 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 98.04 + }, + "pen": { + "Pr@50": 88.89, + "Pr@60": 88.89, + "Pr@70": 88.89, + "Pr@80": 88.89, + "Pr@90": 66.67, + "IoU": 83.41 + }, + "plate": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 92.31, + "Pr@80": 92.31, + "Pr@90": 92.31, + "IoU": 92.77 + }, + "pringles": { + "Pr@50": 90.0, + "Pr@60": 90.0, + "Pr@70": 90.0, + "Pr@80": 90.0, + "Pr@90": 90.0, + "IoU": 74.47 + }, + "scissors": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 80.0, + "IoU": 90.58 + }, + "sponge": { + "Pr@50": 88.89, + "Pr@60": 88.89, + "Pr@70": 88.89, + "Pr@80": 88.89, + "Pr@90": 88.89, + "IoU": 86.0 + }, + "spoon": { + "Pr@50": 75.0, + "Pr@60": 75.0, + "Pr@70": 75.0, + "Pr@80": 75.0, + "Pr@90": 0.0, + "IoU": 59.12 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_results.json b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_results.json new file mode 100644 index 0000000000..556cd0b73a --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 91.36, + "mPr@60": 90.31, + "mPr@70": 88.03, + "mPr@80": 86.86, + "mPr@90": 78.26, + "mIoU": 85.71 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..9fd653ba03 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py @@ -0,0 +1,577 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/20241010_013328/20241010_013328.json b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/20241010_013328/20241010_013328.json new file mode 100644 index 0000000000..ef5053715f --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/20241010_013328/20241010_013328.json @@ -0,0 +1 @@ +{"mPr@50": 95.07, "mPr@60": 95.07, "mPr@70": 94.37, "mPr@80": 90.92, "mPr@90": 76.8, "mIoU": 88.28, "data_time": 0.9449685159183684, "time": 1.1999771822066534} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..b3c474c5b0 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4352 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.0020218618254666853 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.0030472641418587874 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 0.04081349300446145 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.01680336699119961 + }, + { + "gt_label": "_background_", + "pred_label": "can", + "score": 0.011035478961494035 + }, + { + "gt_label": "_background_", + "pred_label": "cap", + "score": 0.008868903099439754 + }, + { + "gt_label": "_background_", + "pred_label": "cereal", + "score": 0.010808071189713527 + }, + { + "gt_label": "_background_", + "pred_label": "cup", + "score": 0.009600742655897021 + }, + { + "gt_label": "_background_", + "pred_label": "flashlight", + "score": 0.012846471762218795 + }, + { + "gt_label": "_background_", + "pred_label": "fork", + "score": 0.0008021292313712411 + }, + { + "gt_label": "_background_", + "pred_label": "juice_box", + "score": 0.02209576604354594 + }, + { + "gt_label": "_background_", + "pred_label": "keyboard", + "score": 0.005643847426916208 + }, + { + "gt_label": "_background_", + "pred_label": "knife", + "score": 0.0002852933864155445 + }, + { + "gt_label": "_background_", + "pred_label": "laptop", + "score": 0.00768638268618112 + }, + { + "gt_label": "_background_", + "pred_label": "lemon", + "score": 0.0036840059028442054 + }, + { + "gt_label": "_background_", + "pred_label": "marker", + "score": 0.004341421097627852 + }, + { + "gt_label": "_background_", + "pred_label": "milk", + "score": 0.0022410002237279007 + }, + { + "gt_label": "_background_", + "pred_label": "monitor", + "score": 0.00483758350878532 + }, + { + "gt_label": "_background_", + "pred_label": "mouse", + "score": 0.0019515721505527106 + }, + { + "gt_label": "_background_", + "pred_label": "mug", + "score": 0.007798019228691552 + }, + { + "gt_label": "_background_", + "pred_label": "orange", + "score": 0.002931492912588711 + }, + { + "gt_label": "_background_", + "pred_label": "peach", + "score": 0.0037997771321142816 + }, + { + "gt_label": "_background_", + "pred_label": "pear", + "score": 0.001414062871798786 + }, + { + "gt_label": "_background_", + "pred_label": "pen", + "score": 0.0020384005725052673 + }, + { + "gt_label": "_background_", + "pred_label": "plate", + "score": 0.013466674776165631 + }, + { + "gt_label": "_background_", + "pred_label": "pringles", + "score": 0.004200841747799902 + }, + { + "gt_label": "_background_", + "pred_label": "scissors", + "score": 0.00422151518159813 + }, + { + "gt_label": "_background_", + "pred_label": "sponge", + "score": 0.003696409963123142 + }, + { + "gt_label": "_background_", + "pred_label": "spoon", + "score": 0.0009096310871220261 + }, + { + "gt_label": "apple", + "pred_label": "_background_", + "score": 1.6573049329848173 + }, + { + "gt_label": "apple", + "pred_label": "banana", + "score": 0.0 + }, + { + "gt_label": "apple", + "pred_label": "book", + "score": 0.0 + }, + { + "gt_label": 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b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_irl_vision_cat_irl_vision_irl_vision_cat.py new file mode 100644 index 0000000000..b3d168502f --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_irl_vision_cat_irl_vision_irl_vision_cat.py @@ -0,0 +1,334 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_label_results.json b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_label_results.json new file mode 100644 index 0000000000..b4c72f63ed --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_label_results.json @@ -0,0 +1,242 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 99.69 + }, + "apple": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.74 + }, + "banana": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.78 + }, + "book": { + "Pr@50": 93.1, + "Pr@60": 93.1, + "Pr@70": 93.1, + "Pr@80": 93.1, + "Pr@90": 86.21, + "IoU": 88.98 + }, + "bowl": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.72 + }, + "can": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 90.48, + "IoU": 94.88 + }, + "cap": { + "Pr@50": 92.86, + "Pr@60": 92.86, + "Pr@70": 92.86, + "Pr@80": 92.86, + "Pr@90": 92.86, + "IoU": 92.7 + }, + "cereal": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 84.62, + "IoU": 92.31 + }, + "cup": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 95.45, + "IoU": 93.82 + }, + "flashlight": { + "Pr@50": 93.75, + "Pr@60": 93.75, + "Pr@70": 93.75, + "Pr@80": 87.5, + "Pr@90": 68.75, + "IoU": 89.55 + }, + "fork": { + "Pr@50": 75.0, + "Pr@60": 75.0, + "Pr@70": 75.0, + "Pr@80": 75.0, + "Pr@90": 50.0, + "IoU": 66.78 + }, + "juice_box": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 88.89, + "IoU": 94.48 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.3 + }, + "knife": { + "Pr@50": 80.0, + "Pr@60": 80.0, + "Pr@70": 80.0, + "Pr@80": 60.0, + "Pr@90": 20.0, + "IoU": 76.49 + }, + "laptop": { + "Pr@50": 85.71, + "Pr@60": 85.71, + "Pr@70": 85.71, + "Pr@80": 85.71, + "Pr@90": 85.71, + "IoU": 91.65 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 80.0, + "Pr@90": 80.0, + "IoU": 93.92 + }, + "marker": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 92.86, + "Pr@90": 64.29, + "IoU": 82.74 + }, + "milk": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 50.0, + "IoU": 88.82 + }, + "monitor": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 98.69 + }, + "mouse": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 75.0, + "Pr@90": 25.0, + "IoU": 85.82 + }, + "mug": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 94.97 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 75.0, + "IoU": 95.85 + }, + "peach": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 66.67, + "IoU": 94.65 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.44 + }, + "pen": { + "Pr@50": 66.67, + "Pr@60": 66.67, + "Pr@70": 55.56, + "Pr@80": 55.56, + "Pr@90": 22.22, + "IoU": 43.42 + }, + "plate": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 93.17 + }, + "pringles": { + "Pr@50": 90.0, + "Pr@60": 90.0, + "Pr@70": 80.0, + "Pr@80": 80.0, + "Pr@90": 80.0, + "IoU": 80.33 + }, + "scissors": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 91.26 + }, + "sponge": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 77.78, + "IoU": 93.46 + }, + "spoon": { + "Pr@50": 75.0, + "Pr@60": 75.0, + "Pr@70": 75.0, + "Pr@80": 50.0, + "Pr@90": 0.0, + "IoU": 47.0 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_results.json b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_results.json new file mode 100644 index 0000000000..028fd4fac5 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 95.07, + "mPr@60": 95.07, + "mPr@70": 94.37, + "mPr@80": 90.92, + "mPr@90": 76.8, + "mIoU": 88.28 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..b3d168502f --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/show/vis_data/config.py @@ -0,0 +1,334 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20241010_013719/20241010_013719.json b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20241010_013719/20241010_013719.json new file mode 100644 index 0000000000..7e6a7283be --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/20241010_013719/20241010_013719.json @@ -0,0 +1 @@ +{"mPr@50": 96.88, "mPr@60": 96.21, "mPr@70": 94.79, "mPr@80": 91.16, "mPr@90": 79.81, "mIoU": 89.04, "data_time": 0.9697185556093851, "time": 1.2300909019651867} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..eaba9ad630 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4352 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.0010915573045464312 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.00364265903524775 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 0.03674082654621057 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.01504612511835024 + }, + { + "gt_label": "_background_", + "pred_label": "can", + "score": 0.007752537674335449 + }, + { + "gt_label": "_background_", + "pred_label": "cap", + "score": 0.007297722130774436 + }, + { + "gt_label": "_background_", + "pred_label": "cereal", + "score": 0.006859445334252006 + }, + { + "gt_label": "_background_", + "pred_label": "cup", + "score": 0.008930923400834439 + }, + { + "gt_label": "_background_", + "pred_label": "flashlight", + "score": 0.0065741519478364615 + }, + { + "gt_label": "_background_", + "pred_label": "fork", + "score": 0.0006904926888608106 + }, + { + "gt_label": "_background_", + "pred_label": "juice_box", + "score": 0.018002426151496824 + }, + { + "gt_label": "_background_", + "pred_label": "keyboard", + "score": 0.005829908331100258 + }, + { + "gt_label": "_background_", + "pred_label": "knife", + "score": 0.001075018557507849 + }, + { + "gt_label": "_background_", + "pred_label": "laptop", + "score": 0.007409358673284867 + }, + { + "gt_label": "_background_", + "pred_label": "lemon", + 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'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=72, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_label_results.json b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_label_results.json new file mode 100644 index 0000000000..9a68801656 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_label_results.json @@ -0,0 +1,242 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 99.7 + }, + "apple": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.59 + }, + "banana": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.07 + }, + "book": { + "Pr@50": 93.1, + "Pr@60": 93.1, + "Pr@70": 89.66, + "Pr@80": 89.66, + "Pr@90": 82.76, + "IoU": 88.41 + }, + "bowl": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.18 + }, + "can": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.37 + }, + "cap": { + "Pr@50": 92.86, + "Pr@60": 92.86, + "Pr@70": 92.86, + "Pr@80": 92.86, + "Pr@90": 92.86, + "IoU": 93.49 + }, + "cereal": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 91.65 + }, + "cup": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.86 + }, + "flashlight": { + "Pr@50": 87.5, + "Pr@60": 87.5, + "Pr@70": 87.5, + "Pr@80": 81.25, + "Pr@90": 81.25, + "IoU": 92.03 + }, + "fork": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 25.0, + "IoU": 56.79 + }, + "juice_box": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 88.89, + "IoU": 94.74 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.47 + }, + "knife": { + "Pr@50": 100.0, + "Pr@60": 80.0, + "Pr@70": 80.0, + "Pr@80": 60.0, + "Pr@90": 0.0, + "IoU": 78.44 + }, + "laptop": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 85.71, + "Pr@80": 85.71, + "Pr@90": 85.71, + "IoU": 93.5 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 80.0, + "Pr@90": 80.0, + "IoU": 93.14 + }, + "marker": { + "Pr@50": 92.86, + "Pr@60": 92.86, + "Pr@70": 92.86, + "Pr@80": 92.86, + "Pr@90": 71.43, + "IoU": 82.98 + }, + "milk": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 87.41 + }, + "monitor": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 98.15 + }, + "mouse": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 75.0, + "Pr@80": 37.5, + "Pr@90": 0.0, + "IoU": 80.34 + }, + "mug": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 94.12, + "IoU": 96.26 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.44 + }, + "peach": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.5 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.76 + }, + "pen": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 22.22, + "IoU": 57.21 + }, + "plate": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 92.54 + }, + "pringles": { + "Pr@50": 90.0, + "Pr@60": 90.0, + "Pr@70": 90.0, + "Pr@80": 90.0, + "Pr@90": 90.0, + "IoU": 82.88 + }, + "scissors": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 80.0, + "IoU": 93.33 + }, + "sponge": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 94.34 + }, + "spoon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 75.0, + "Pr@90": 0.0, + "IoU": 53.54 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_results.json b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_results.json new file mode 100644 index 0000000000..ab5d499d0e --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 96.88, + "mPr@60": 96.21, + "mPr@70": 94.79, + "mPr@80": 91.16, + "mPr@90": 79.81, + "mIoU": 89.04 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..ef9655828f --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py @@ -0,0 +1,356 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=72, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/pred_results', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show', + target_dataset='IRL_VISION_CAT', + test_dataset='IRL_VISION_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20241010_020147/20241010_020147.json b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20241010_020147/20241010_020147.json new file mode 100644 index 0000000000..213d2efb27 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20241010_020147/20241010_020147.json @@ -0,0 +1 @@ +{"mPr@50": 18.97, "mPr@60": 15.22, "mPr@70": 10.15, "mPr@80": 8.08, "mPr@90": 1.67, "mIoU": 19.98, "data_time": 0.9740786832921645, "time": 1.312850741779103} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_hots_cat_irl_vision_cat_hots_cat.py b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_hots_cat_irl_vision_cat_hots_cat.py new file mode 100644 index 0000000000..86a89c0745 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_hots_cat_irl_vision_cat_hots_cat.py @@ -0,0 +1,348 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION_CAT', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_label_results.json b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_label_results.json new file mode 100644 index 0000000000..aebea1bd7c --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_label_results.json @@ -0,0 +1,210 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 88.24, + "Pr@90": 23.53, + "IoU": 87.01 + }, + "apple": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 7.94 + }, + "banana": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 33.33, + "Pr@80": 33.33, + "Pr@90": 0.0, + "IoU": 42.01 + }, + "book": { + "Pr@50": 62.5, + "Pr@60": 25.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 35.35 + }, + "bowl": { + "Pr@50": 16.67, + "Pr@60": 16.67, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 15.79 + }, + "can": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 10.14 + }, + "cup": { + "Pr@50": 11.11, + "Pr@60": 11.11, + "Pr@70": 11.11, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 27.27 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "juice_box": { + "Pr@50": 42.86, + "Pr@60": 42.86, + "Pr@70": 42.86, + "Pr@80": 28.57, + "Pr@90": 0.0, + "IoU": 47.88 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 80.0, + "Pr@70": 60.0, + "Pr@80": 60.0, + "Pr@90": 20.0, + "IoU": 69.5 + }, + "knife": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "laptop": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 25.28 + }, + "lemon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "marker": { + "Pr@50": 20.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 15.56 + }, + "milk": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 17.62 + }, + "monitor": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.14 + }, + "mouse": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "orange": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 10.27 + }, + "peach": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 28.84 + }, + "pear": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pen": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "plate": { + "Pr@50": 40.0, + "Pr@60": 20.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 35.81 + }, + "pringles": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 16.67, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 42.88 + }, + "scissors": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.13 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "stapler": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_results.json b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_results.json new file mode 100644 index 0000000000..160a9a7944 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 18.97, + "mPr@60": 15.22, + "mPr@70": 10.15, + "mPr@80": 8.08, + "mPr@90": 1.67, + "mIoU": 19.98 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..f96a94d56f --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json @@ -0,0 +1,3252 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.04076895586772229 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.004002004278634048 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 2.478949106441378 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.03152456001941557 + }, + { + "gt_label": "_background_", + "pred_label": "can", + "score": 0.1364894090818349 + }, + { + "gt_label": "_background_", + "pred_label": "cup", + "score": 0.09923098328308985 + }, + { + "gt_label": "_background_", + "pred_label": "fork", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "juice_box", + "score": 0.15146767070947112 + }, + { + "gt_label": "_background_", + 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b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..86a89c0745 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py @@ -0,0 +1,348 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION_CAT', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241010_020252/20241010_020252.json b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241010_020252/20241010_020252.json new file mode 100644 index 0000000000..85c09c5e63 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241010_020252/20241010_020252.json @@ -0,0 +1 @@ +{"mPr@50": 25.46, "mPr@60": 23.76, "mPr@70": 19.09, "mPr@80": 15.02, "mPr@90": 6.42, "mIoU": 28.27, "data_time": 0.9832270986893598, "time": 1.3917224407196045} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..e389d1fb00 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json @@ -0,0 +1,3252 @@ +[ + { + "gt_label": "_background_", + 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b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..bf9ba8ca80 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "scissors", + "pred_label": "spoon", + "score": 68.1041367193739 + }, + { + "gt_label": "plate", + "pred_label": "bowl", + "score": 64.93130006689881 + }, + { + "gt_label": "laptop", + "pred_label": "monitor", + "score": 59.37222324085697 + }, + { + "gt_label": "stapler", + "pred_label": "pringles", + "score": 57.98425196850394 + }, + { + "gt_label": "mouse", + "pred_label": "spoon", + "score": 52.227674190382736 + }, + { + "gt_label": "peach", + "pred_label": "lemon", + "score": 46.07133948615615 + }, + { + "gt_label": "can", + "pred_label": 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dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION_CAT', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_label_results.json b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_label_results.json new file mode 100644 index 0000000000..4e4314555c --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_label_results.json @@ -0,0 +1,210 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 82.35, + "IoU": 95.58 + }, + "apple": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "banana": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 33.33, + "Pr@80": 33.33, + "Pr@90": 0.0, + "IoU": 44.1 + }, + "book": { + "Pr@50": 87.5, + "Pr@60": 87.5, + "Pr@70": 50.0, + "Pr@80": 37.5, + "Pr@90": 25.0, + "IoU": 60.27 + }, + "bowl": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 12.49 + }, + "can": { + "Pr@50": 30.0, + "Pr@60": 20.0, + "Pr@70": 20.0, + "Pr@80": 20.0, + "Pr@90": 0.0, + "IoU": 27.27 + }, + "cup": { + "Pr@50": 77.78, + "Pr@60": 77.78, + "Pr@70": 55.56, + "Pr@80": 33.33, + "Pr@90": 11.11, + "IoU": 64.99 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "juice_box": { + "Pr@50": 100.0, + "Pr@60": 85.71, + "Pr@70": 85.71, + "Pr@80": 71.43, + "Pr@90": 28.57, + "IoU": 80.14 + }, + "keyboard": { + "Pr@50": 60.0, + "Pr@60": 60.0, + "Pr@70": 40.0, + "Pr@80": 20.0, + "Pr@90": 20.0, + "IoU": 49.84 + }, + "knife": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 14.1 + }, + "laptop": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "lemon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 17.42 + }, + "marker": { + "Pr@50": 20.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 9.4 + }, + "milk": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 25.0, + "Pr@80": 25.0, + "Pr@90": 0.0, + "IoU": 46.35 + }, + "monitor": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 22.61 + }, + "mouse": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 7.99 + }, + "orange": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 27.44 + }, + "peach": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 33.33, + "Pr@80": 33.33, + "Pr@90": 0.0, + "IoU": 20.71 + }, + "pear": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 31.7 + }, + "pen": { + "Pr@50": 16.67, + "Pr@60": 16.67, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 26.09 + }, + "plate": { + "Pr@50": 20.0, + "Pr@60": 20.0, + "Pr@70": 20.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 31.05 + }, + "pringles": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 33.33, + "Pr@80": 16.67, + "Pr@90": 0.0, + "IoU": 38.21 + }, + "scissors": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 7.19 + }, + "stapler": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_results.json b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_results.json new file mode 100644 index 0000000000..cee7b386c1 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 25.46, + "mPr@60": 23.76, + "mPr@70": 19.09, + "mPr@80": 15.02, + "mPr@90": 6.42, + "mIoU": 28.27 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..c13860d3ea --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py @@ -0,0 +1,445 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION_CAT', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241010_020358/20241010_020358.json b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241010_020358/20241010_020358.json new file mode 100644 index 0000000000..062159519a --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241010_020358/20241010_020358.json @@ -0,0 +1 @@ +{"mPr@50": 19.66, "mPr@60": 17.06, "mPr@70": 12.79, "mPr@80": 12.24, "mPr@90": 7.66, "mIoU": 23.07, "data_time": 0.9493998078738942, "time": 1.321636340197395} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..36613680a0 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json @@ -0,0 +1,3252 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.04462318678816716 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.014648198704453403 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 0.06661888452328889 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.2338329866670972 + }, + { + "gt_label": "_background_", + "pred_label": "can", + "score": 0.06484050896172586 + }, + { + "gt_label": "_background_", + "pred_label": "cup", + "score": 0.04415519321933478 + }, + { + "gt_label": "_background_", + "pred_label": "fork", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "juice_box", + "score": 0.30349382938779657 + }, + { + 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0000000000..4517928454 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_hots_cat_irl_vision_cat_hots_cat.py @@ -0,0 +1,535 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION_CAT', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_label_results.json b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_label_results.json new file mode 100644 index 0000000000..cf4b519341 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_label_results.json @@ -0,0 +1,210 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 93.87 + }, + "apple": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 8.23 + }, + "banana": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 37.5 + }, + "book": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 25.0, + "IoU": 54.73 + }, + "bowl": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 18.79 + }, + "can": { + "Pr@50": 30.0, + "Pr@60": 10.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 12.93 + }, + "cup": { + "Pr@50": 22.22, + "Pr@60": 11.11, + "Pr@70": 11.11, + "Pr@80": 11.11, + "Pr@90": 0.0, + "IoU": 23.17 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "juice_box": { + "Pr@50": 85.71, + "Pr@60": 85.71, + "Pr@70": 71.43, + "Pr@80": 57.14, + "Pr@90": 14.29, + "IoU": 66.59 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 60.0, + "IoU": 91.72 + }, + "knife": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "laptop": { + "Pr@50": 20.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 42.9 + }, + "lemon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 18.32 + }, + "marker": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 19.94 + }, + "milk": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "monitor": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.96 + }, + "mouse": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 15.3 + }, + "orange": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "peach": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 13.46 + }, + "pear": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pen": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 9.39 + }, + "plate": { + "Pr@50": 20.0, + "Pr@60": 20.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 20.6 + }, + "pringles": { + "Pr@50": 50.0, + "Pr@60": 33.33, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 47.94 + }, + "scissors": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 3.6 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "stapler": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_results.json b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_results.json new file mode 100644 index 0000000000..94f90b99a5 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 19.66, + "mPr@60": 17.06, + "mPr@70": 12.79, + "mPr@80": 12.24, + "mPr@90": 7.66, + "mIoU": 23.07 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..4517928454 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py @@ -0,0 +1,535 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION_CAT', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20241010_020504/20241010_020504.json b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20241010_020504/20241010_020504.json new file mode 100644 index 0000000000..bc5e0cff6a --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20241010_020504/20241010_020504.json @@ -0,0 +1 @@ +{"mPr@50": 24.58, "mPr@60": 16.05, "mPr@70": 10.82, "mPr@80": 7.47, "mPr@90": 2.58, "mIoU": 23.98, "data_time": 0.9960396009332994, "time": 1.3699571525349337} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..8e5acfb5c6 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json @@ -0,0 +1,3252 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.03406643141046475 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.0034814117147868636 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 1.577336523770682 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.10252640674150845 + }, + { + "gt_label": "_background_", + "pred_label": "can", + "score": 0.14311172317496337 + }, + { + "gt_label": "_background_", + "pred_label": "cup", + "score": 0.002009405419272955 + }, + { + "gt_label": "_background_", + "pred_label": "fork", + "score": 0.01116855570247061 + }, + { + "gt_label": "_background_", + "pred_label": "juice_box", + "score": 0.5662317550076836 + }, + { + "gt_label": "_background_", + "pred_label": "keyboard", + "score": 0.18435126462864668 + }, + { + "gt_label": "_background_", + "pred_label": "knife", + "score": 0.039487153006642954 + }, + { + "gt_label": "_background_", + "pred_label": "laptop", + "score": 3.0290618436575105 + }, + { + "gt_label": "_background_", + "pred_label": "lemon", + "score": 0.007453492194745031 + }, + { + "gt_label": "_background_", + "pred_label": "marker", + "score": 0.1086480837165028 + }, + { + "gt_label": "_background_", + "pred_label": "milk", + "score": 0.005046878727476259 + }, + { + "gt_label": "_background_", + "pred_label": "monitor", + "score": 1.6374784952731074 + }, + { + "gt_label": "_background_", + "pred_label": "mouse", + "score": 0.07511905142979709 + }, + { + "gt_label": "_background_", + "pred_label": "orange", + "score": 0.0019393098813913403 + }, + { + "gt_label": "_background_", + "pred_label": "peach", + "score": 0.06897400927550888 + }, + { + "gt_label": "_background_", + "pred_label": "pear", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "pen", + "score": 0.02708024280159715 + }, + { + "gt_label": "_background_", + "pred_label": "plate", + "score": 0.18166426900985147 + }, + { + "gt_label": "_background_", + "pred_label": "pringles", + "score": 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b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_hots_cat_irl_vision_cat_hots_cat.py new file mode 100644 index 0000000000..59b4120d84 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_hots_cat_irl_vision_cat_hots_cat.py @@ -0,0 +1,334 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION_CAT', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_label_results.json b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_label_results.json new file mode 100644 index 0000000000..584873e856 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_label_results.json @@ -0,0 +1,210 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 94.12, + "Pr@90": 47.06, + "IoU": 89.55 + }, + "apple": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 5.5 + }, + "banana": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 33.33, + "Pr@80": 33.33, + "Pr@90": 0.0, + "IoU": 40.2 + }, + "book": { + "Pr@50": 75.0, + "Pr@60": 62.5, + "Pr@70": 25.0, + "Pr@80": 12.5, + "Pr@90": 0.0, + "IoU": 49.8 + }, + "bowl": { + "Pr@50": 33.33, + "Pr@60": 16.67, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 25.2 + }, + "can": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 8.39 + }, + "cup": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 8.77 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 1.16 + }, + "juice_box": { + "Pr@50": 85.71, + "Pr@60": 71.43, + "Pr@70": 42.86, + "Pr@80": 14.29, + "Pr@90": 0.0, + "IoU": 64.52 + }, + "keyboard": { + "Pr@50": 80.0, + "Pr@60": 80.0, + "Pr@70": 60.0, + "Pr@80": 40.0, + "Pr@90": 20.0, + "IoU": 74.17 + }, + "knife": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 2.43 + }, + "laptop": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 28.81 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 59.13 + }, + "marker": { + "Pr@50": 20.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 20.92 + }, + "milk": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 10.93 + }, + "monitor": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.05 + }, + "mouse": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 8.41 + }, + "orange": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.02 + }, + "peach": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 30.21 + }, + "pear": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pen": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 6.39 + }, + "plate": { + "Pr@50": 20.0, + "Pr@60": 20.0, + "Pr@70": 20.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 38.35 + }, + "pringles": { + "Pr@50": 33.33, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 35.92 + }, + "scissors": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 1.24 + }, + "spoon": { + "Pr@50": 25.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 13.54 + }, + "stapler": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_results.json b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_results.json new file mode 100644 index 0000000000..726a955a9d --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 24.58, + "mPr@60": 16.05, + "mPr@70": 10.82, + "mPr@80": 7.47, + "mPr@90": 2.58, + "mIoU": 23.98 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..59b4120d84 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py @@ -0,0 +1,334 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION_CAT', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241010_020609/20241010_020609.json b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241010_020609/20241010_020609.json new file mode 100644 index 0000000000..36a32ded9e --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20241010_020609/20241010_020609.json @@ -0,0 +1 @@ +{"mPr@50": 24.93, "mPr@60": 20.51, "mPr@70": 15.12, "mPr@80": 11.59, "mPr@90": 6.51, "mIoU": 24.5, "data_time": 0.9896413017721737, "time": 1.3458442407495834} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..16224a80de --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json @@ -0,0 +1,3252 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.07300040786787884 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.0 + }, + { + "gt_label": "_background_", + 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a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/top_10_confusion.json b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..f31530c79f --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "orange", + "pred_label": "apple", + "score": 86.5781990521327 + }, + { + "gt_label": "peach", + "pred_label": "apple", + "score": 86.35569967572961 + }, + { + "gt_label": "can", + "pred_label": "pringles", + "score": 83.6472016778894 + }, + { + "gt_label": "monitor", + "pred_label": "laptop", + "score": 76.10400950485979 + }, + { + "gt_label": "spoon", + "pred_label": "knife", + "score": 70.45998404679607 + }, + { + "gt_label": "lemon", + "pred_label": "apple", + "score": 60.0121359223301 + }, + { + "gt_label": "scissors", + "pred_label": "spoon", + "score": 57.5626896661875 + }, + { + "gt_label": "knife", + "pred_label": "pen", + "score": 52.99921073401737 + }, + { + "gt_label": "stapler", + "pred_label": "marker", + "score": 42.39378099450476 + }, + { + "gt_label": "pear", + "pred_label": "orange", + "score": 41.14748328053503 + } +] \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/segnext_hots_cat_irl_vision_cat_hots_cat.py b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/segnext_hots_cat_irl_vision_cat_hots_cat.py new file mode 100644 index 0000000000..39cca053d0 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/segnext_hots_cat_irl_vision_cat_hots_cat.py @@ -0,0 +1,356 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=30, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION_CAT', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/segnext_label_results.json b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/segnext_label_results.json new file mode 100644 index 0000000000..e92355dc32 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/segnext_label_results.json @@ -0,0 +1,210 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 88.24, + "IoU": 94.43 + }, + "apple": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 8.2 + }, + "banana": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "book": { + "Pr@50": 62.5, + "Pr@60": 62.5, + "Pr@70": 62.5, + "Pr@80": 50.0, + "Pr@90": 12.5, + "IoU": 67.46 + }, + "bowl": { + "Pr@50": 33.33, + "Pr@60": 16.67, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 24.17 + }, + "can": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 6.66 + }, + "cup": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 7.12 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 3.19 + }, + "juice_box": { + "Pr@50": 85.71, + "Pr@60": 85.71, + "Pr@70": 85.71, + "Pr@80": 71.43, + "Pr@90": 28.57, + "IoU": 76.09 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 80.0, + "Pr@70": 60.0, + "Pr@80": 60.0, + "Pr@90": 40.0, + "IoU": 78.58 + }, + "knife": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "laptop": { + "Pr@50": 20.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 38.76 + }, + "lemon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "marker": { + "Pr@50": 20.0, + "Pr@60": 20.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 22.9 + }, + "milk": { + "Pr@50": 100.0, + "Pr@60": 75.0, + "Pr@70": 25.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 62.84 + }, + "monitor": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 11.14 + }, + "mouse": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 3.04 + }, + "orange": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "peach": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 1.46 + }, + "pear": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pen": { + "Pr@50": 33.33, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 29.51 + }, + "plate": { + "Pr@50": 60.0, + "Pr@60": 60.0, + "Pr@70": 60.0, + "Pr@80": 20.0, + "Pr@90": 0.0, + "IoU": 56.71 + }, + "pringles": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 44.06 + }, + "scissors": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.73 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "stapler": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/segnext_results.json b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/segnext_results.json new file mode 100644 index 0000000000..33a5f2c6b9 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/segnext_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 24.93, + "mPr@60": 20.51, + "mPr@70": 15.12, + "mPr@80": 11.59, + "mPr@90": 6.51, + "mIoU": 24.5 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..39cca053d0 --- /dev/null +++ b/my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py @@ -0,0 +1,356 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=30, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dataset='IRL_VISION_CAT', + output_dir= + 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='IRL_VISION_CAT', + save_dir= + 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS_CAT', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/conversion_tests/zero_shot/models/hots_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py b/my_projects/conversion_tests/zero_shot/models/hots_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py new file mode 100644 index 0000000000..00105c9456 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/models/hots_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py @@ -0,0 +1,325 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=171, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=171, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=171, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1000, start_factor=0.1, type='LinearLR'), + dict( + begin=1000, + by_epoch=False, + end=160000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='images/train2017', seg_map_path='annotations/train2017'), + data_root='data/coco_stuff164k', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='COCOStuffDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/val2017', seg_map_path='annotations/val2017'), + data_root='data/coco_stuff164k', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='COCOStuffDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) diff --git a/my_projects/conversion_tests/zero_shot/models/hots_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512.py b/my_projects/conversion_tests/zero_shot/models/hots_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..fbf65da0d9 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/models/hots_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,567 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.1, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=150, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 150 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=160000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=5000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + max_size=2048, + resize_type='ResizeShortestEdge', + scales=[ + 256, + 307, + 358, + 409, + 460, + 512, + 563, + 614, + 665, + 716, + 768, + 819, + 870, + 921, + 972, + 1024, + ], + type='RandomChoiceResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + max_size=2048, + resize_type='ResizeShortestEdge', + scales=[ + 256, + 307, + 358, + 409, + 460, + 512, + 563, + 614, + 665, + 716, + 768, + 819, + 870, + 921, + 972, + 1024, + ], + type='RandomChoiceResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) diff --git a/my_projects/conversion_tests/zero_shot/models/hots_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py b/my_projects/conversion_tests/zero_shot/models/hots_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..94098474af --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/models/hots_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,630 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=224, + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.1, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=150, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 150 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) diff --git a/my_projects/conversion_tests/zero_shot/models/hots_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_mit-b0_8xb2-160k_ade20k-512x512.py b/my_projects/conversion_tests/zero_shot/models/hots_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_mit-b0_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..d40c7c82e3 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/models/hots_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_mit-b0_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,312 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) diff --git a/my_projects/conversion_tests/zero_shot/models/hots_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py b/my_projects/conversion_tests/zero_shot/models/hots_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py new file mode 100644 index 0000000000..c373008a46 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/models/hots_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py @@ -0,0 +1,334 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=150, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=16, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) diff --git a/my_projects/conversion_tests/zero_shot/models/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py b/my_projects/conversion_tests/zero_shot/models/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py new file mode 100644 index 0000000000..276e8864bb --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/models/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py @@ -0,0 +1,325 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=171, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=171, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=171, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1000, start_factor=0.1, type='LinearLR'), + dict( + begin=1000, + by_epoch=False, + end=160000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='images/train2017', seg_map_path='annotations/train2017'), + data_root='data/coco_stuff164k', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='COCOStuffDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/val2017', seg_map_path='annotations/val2017'), + data_root='data/coco_stuff164k', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='COCOStuffDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) diff --git a/my_projects/conversion_tests/zero_shot/models/irl_vision_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512.py b/my_projects/conversion_tests/zero_shot/models/irl_vision_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..0c4aa5a2c5 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/models/irl_vision_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,567 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.1, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=150, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 150 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=160000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=5000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + max_size=2048, + resize_type='ResizeShortestEdge', + scales=[ + 256, + 307, + 358, + 409, + 460, + 512, + 563, + 614, + 665, + 716, + 768, + 819, + 870, + 921, + 972, + 1024, + ], + type='RandomChoiceResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + max_size=2048, + resize_type='ResizeShortestEdge', + scales=[ + 256, + 307, + 358, + 409, + 460, + 512, + 563, + 614, + 665, + 716, + 768, + 819, + 870, + 921, + 972, + 1024, + ], + type='RandomChoiceResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) diff --git a/my_projects/conversion_tests/zero_shot/models/irl_vision_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py b/my_projects/conversion_tests/zero_shot/models/irl_vision_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..be45e8a5fd --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/models/irl_vision_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,630 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=224, + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.1, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=150, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 150 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) diff --git a/my_projects/conversion_tests/zero_shot/models/irl_vision_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_mit-b0_8xb2-160k_ade20k-512x512.py b/my_projects/conversion_tests/zero_shot/models/irl_vision_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_mit-b0_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..9f5434620e --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/models/irl_vision_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_mit-b0_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,312 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) diff --git a/my_projects/conversion_tests/zero_shot/models/irl_vision_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py b/my_projects/conversion_tests/zero_shot/models/irl_vision_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py new file mode 100644 index 0000000000..90d50b7b67 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/models/irl_vision_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py @@ -0,0 +1,334 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=150, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=16, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/20241029_085504/20241029_085504.json b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/20241029_085504/20241029_085504.json new file mode 100644 index 0000000000..9251a04fee --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/20241029_085504/20241029_085504.json @@ -0,0 +1 @@ +{"mPr@50": 0.0, "mPr@60": 0.0, "mPr@70": 0.0, "mPr@80": 0.0, "mPr@90": 0.0, "mIoU": 0.01, "data_time": 0.4220413179958568, "time": 0.6004405722898596} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_label_results.json b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_label_results.json new file mode 100644 index 0000000000..06a9958e1b --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_label_results.json @@ -0,0 +1,210 @@ +{ + "_background_": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.3 + }, + "apple": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "banana": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "book": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "bowl": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "can": { + "Pr@50": 0.0, + 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b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/zero_shot/models/hots_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=171, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=171, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=171, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1000, start_factor=0.1, type='LinearLR'), + dict( + begin=1000, + by_epoch=False, + end=160000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'work_dirs/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='images/train2017', seg_map_path='annotations/train2017'), + data_root='data/coco_stuff164k', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='COCOStuffDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/val2017', seg_map_path='annotations/val2017'), + data_root='data/coco_stuff164k', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='COCOStuffDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'work_dirs/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'work_dirs/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_results.json b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_results.json new file mode 100644 index 0000000000..45a91b000c --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 0.0, + "mPr@60": 0.0, + "mPr@70": 0.0, + "mPr@80": 0.0, + "mPr@90": 0.0, + "mIoU": 0.01 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/show/vis_data/config.py b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..c856d85caf --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/show/vis_data/config.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/zero_shot/models/hots_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=171, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=171, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=171, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1000, start_factor=0.1, type='LinearLR'), + dict( + begin=1000, + by_epoch=False, + end=160000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'work_dirs/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='images/train2017', seg_map_path='annotations/train2017'), + data_root='data/coco_stuff164k', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='COCOStuffDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/val2017', seg_map_path='annotations/val2017'), + data_root='data/coco_stuff164k', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='COCOStuffDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'work_dirs/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'work_dirs/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/mask2former_r50_8xb2-160k_ade20k-512x512/20241029_085537/20241029_085537.json b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/mask2former_r50_8xb2-160k_ade20k-512x512/20241029_085537/20241029_085537.json new file mode 100644 index 0000000000..27ec792907 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/mask2former_r50_8xb2-160k_ade20k-512x512/20241029_085537/20241029_085537.json @@ -0,0 +1 @@ +{"mPr@50": 0.0, "mPr@60": 0.0, "mPr@70": 0.0, "mPr@80": 0.0, "mPr@90": 0.0, "mIoU": 0.09, "data_time": 0.7504651826970717, "time": 0.9513118828044218} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_label_results.json b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_label_results.json new file mode 100644 index 0000000000..e66872d2d3 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_label_results.json @@ -0,0 +1,210 @@ +{ + "_background_": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 2.31 + }, + "apple": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "banana": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "book": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "bowl": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "can": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cup": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "juice_box": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "keyboard": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "knife": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "laptop": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "lemon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "marker": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "milk": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "monitor": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.06 + }, + "mouse": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "orange": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "peach": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pear": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pen": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "plate": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pringles": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "scissors": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "stapler": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512.py b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..ca39b0357f --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,574 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/zero_shot/models/hots_cat/mask2former_r50_8xb2-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.1, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=150, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 150 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=160000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'work_dirs/mask2former_r50_8xb2-160k_ade20k-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=5000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + max_size=2048, + resize_type='ResizeShortestEdge', + scales=[ + 256, + 307, + 358, + 409, + 460, + 512, + 563, + 614, + 665, + 716, + 768, + 819, + 870, + 921, + 972, + 1024, + ], + type='RandomChoiceResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + max_size=2048, + resize_type='ResizeShortestEdge', + scales=[ + 256, + 307, + 358, + 409, + 460, + 512, + 563, + 614, + 665, + 716, + 768, + 819, + 870, + 921, + 972, + 1024, + ], + type='RandomChoiceResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir='work_dirs/mask2former_r50_8xb2-160k_ade20k-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'work_dirs/mask2former_r50_8xb2-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_results.json b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_results.json new file mode 100644 index 0000000000..3b9ffb8e0b --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 0.0, + "mPr@60": 0.0, + "mPr@70": 0.0, + "mPr@80": 0.0, + "mPr@90": 0.0, + "mIoU": 0.09 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/mask2former_r50_8xb2-160k_ade20k-512x512/show/vis_data/config.py b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/mask2former_r50_8xb2-160k_ade20k-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..ca39b0357f --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/mask2former_r50_8xb2-160k_ade20k-512x512/show/vis_data/config.py @@ -0,0 +1,574 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/zero_shot/models/hots_cat/mask2former_r50_8xb2-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.1, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=150, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 150 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=160000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'work_dirs/mask2former_r50_8xb2-160k_ade20k-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=5000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + max_size=2048, + resize_type='ResizeShortestEdge', + scales=[ + 256, + 307, + 358, + 409, + 460, + 512, + 563, + 614, + 665, + 716, + 768, + 819, + 870, + 921, + 972, + 1024, + ], + type='RandomChoiceResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + max_size=2048, + resize_type='ResizeShortestEdge', + scales=[ + 256, + 307, + 358, + 409, + 460, + 512, + 563, + 614, + 665, + 716, + 768, + 819, + 870, + 921, + 972, + 1024, + ], + type='RandomChoiceResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir='work_dirs/mask2former_r50_8xb2-160k_ade20k-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'work_dirs/mask2former_r50_8xb2-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/20241029_085602/20241029_085602.json b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/20241029_085602/20241029_085602.json new file mode 100644 index 0000000000..e1163a57ab --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/20241029_085602/20241029_085602.json @@ -0,0 +1 @@ +{"mPr@50": 0.0, "mPr@60": 0.0, "mPr@70": 0.0, "mPr@80": 0.0, "mPr@90": 0.0, "mIoU": 0.16, "data_time": 0.6959778841804055, "time": 0.8675266574410831} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_label_results.json 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diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..1cc8693ae4 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,638 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/zero_shot/models/hots_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=224, + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.1, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=150, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 150 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'work_dirs/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'work_dirs/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'work_dirs/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/show/vis_data/config.py b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..1cc8693ae4 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/show/vis_data/config.py @@ -0,0 +1,638 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/zero_shot/models/hots_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=224, + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.1, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=150, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 150 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'work_dirs/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'work_dirs/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'work_dirs/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/20241029_085628/20241029_085628.json b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/20241029_085628/20241029_085628.json new file mode 100644 index 0000000000..82d1598a73 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/20241029_085628/20241029_085628.json @@ -0,0 +1 @@ +{"mPr@50": 0.0, "mPr@60": 0.0, "mPr@70": 0.0, "mPr@80": 0.0, "mPr@90": 0.0, "mIoU": 0.24, "data_time": 0.6439762115478516, "time": 0.7918498656328987} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_label_results.json b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_label_results.json new file mode 100644 index 0000000000..c4edd00e8d --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_label_results.json @@ -0,0 +1,210 @@ +{ + "_background_": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 6.33 + }, + "apple": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "banana": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "book": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.03 + }, + "bowl": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "can": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cup": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 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"Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "orange": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "peach": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pear": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pen": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "plate": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pringles": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "scissors": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "stapler": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_mit-b0_8xb2-160k_ade20k-512x512.py b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_mit-b0_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..0e77f986bc --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_mit-b0_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/zero_shot/models/hots_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'work_dirs/segformer_mit-b0_8xb2-160k_ade20k-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir='work_dirs/segformer_mit-b0_8xb2-160k_ade20k-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'work_dirs/segformer_mit-b0_8xb2-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_results.json b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_results.json new file mode 100644 index 0000000000..e43a62c91b --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 0.0, + "mPr@60": 0.0, + "mPr@70": 0.0, + "mPr@80": 0.0, + "mPr@90": 0.0, + "mIoU": 0.24 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/show/vis_data/config.py b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..0e77f986bc --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/show/vis_data/config.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/zero_shot/models/hots_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'work_dirs/segformer_mit-b0_8xb2-160k_ade20k-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir='work_dirs/segformer_mit-b0_8xb2-160k_ade20k-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'work_dirs/segformer_mit-b0_8xb2-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/20241029_085650/20241029_085650.json b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/20241029_085650/20241029_085650.json new file mode 100644 index 0000000000..74c2150ba7 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/20241029_085650/20241029_085650.json @@ -0,0 +1 @@ +{"mPr@50": 0.0, "mPr@60": 0.0, "mPr@70": 0.0, "mPr@80": 0.0, "mPr@90": 0.0, "mIoU": 0.21, "data_time": 0.644719123840332, "time": 0.7868799602284151} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_label_results.json b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_label_results.json new file mode 100644 index 0000000000..7eb530d6a4 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_label_results.json @@ -0,0 +1,210 @@ +{ + "_background_": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 5.31 + }, + "apple": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "banana": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "book": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.03 + }, + "bowl": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "can": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cup": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "juice_box": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "keyboard": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "knife": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "laptop": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "lemon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "marker": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "milk": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "monitor": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "mouse": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "orange": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "peach": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pear": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pen": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "plate": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pringles": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "scissors": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "stapler": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py new file mode 100644 index 0000000000..913c3d967c --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/zero_shot/models/hots_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=150, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'work_dirs/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=16, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir='work_dirs/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'work_dirs/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_results.json b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_results.json new file mode 100644 index 0000000000..dc6a552a4a --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 0.0, + "mPr@60": 0.0, + "mPr@70": 0.0, + "mPr@80": 0.0, + "mPr@90": 0.0, + "mIoU": 0.21 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/show/vis_data/config.py b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..913c3d967c --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/show/vis_data/config.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +dataset_type = 'HOTSCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/zero_shot/models/hots_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=150, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'work_dirs/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=16, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir='work_dirs/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'work_dirs/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/20241029_085930/20241029_085930.json b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/20241029_085930/20241029_085930.json new file mode 100644 index 0000000000..f5e35b9495 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/20241029_085930/20241029_085930.json @@ -0,0 +1 @@ +{"mPr@50": 0.0, "mPr@60": 0.0, "mPr@70": 0.0, "mPr@80": 0.0, "mPr@90": 0.0, "mIoU": 0.0, "data_time": 0.4134111035437811, "time": 0.4572778514453343} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_label_results.json b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_label_results.json new file mode 100644 index 0000000000..108e55e6ae --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_label_results.json @@ -0,0 +1,242 @@ +{ + "_background_": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "apple": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "banana": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "book": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "bowl": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "can": { + 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"pear": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pen": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "plate": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pringles": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "scissors": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "sponge": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py new file mode 100644 index 0000000000..dc7a3f7dec --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/zero_shot/models/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=171, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=171, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=171, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1000, start_factor=0.1, type='LinearLR'), + dict( + begin=1000, + by_epoch=False, + end=160000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'work_dirs/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='images/train2017', seg_map_path='annotations/train2017'), + data_root='data/coco_stuff164k', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='COCOStuffDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/val2017', seg_map_path='annotations/val2017'), + data_root='data/coco_stuff164k', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='COCOStuffDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'work_dirs/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'work_dirs/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_results.json b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_results.json new file mode 100644 index 0000000000..e58208ae1d --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/bisenetv1_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 0.0, + "mPr@60": 0.0, + "mPr@70": 0.0, + "mPr@80": 0.0, + "mPr@90": 0.0, + "mIoU": 0.0 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/show/vis_data/config.py b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..dc7a3f7dec --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/show/vis_data/config.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/zero_shot/models/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=171, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=171, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=171, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1000, start_factor=0.1, type='LinearLR'), + dict( + begin=1000, + by_epoch=False, + end=160000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'work_dirs/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=4, + dataset=dict( + data_prefix=dict( + img_path='images/train2017', seg_map_path='annotations/train2017'), + data_root='data/coco_stuff164k', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='COCOStuffDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/val2017', seg_map_path='annotations/val2017'), + data_root='data/coco_stuff164k', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='COCOStuffDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'work_dirs/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'work_dirs/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/mask2former_r50_8xb2-160k_ade20k-512x512/20241029_090019/20241029_090019.json b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/mask2former_r50_8xb2-160k_ade20k-512x512/20241029_090019/20241029_090019.json new file mode 100644 index 0000000000..3b1599c1af --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/mask2former_r50_8xb2-160k_ade20k-512x512/20241029_090019/20241029_090019.json @@ -0,0 +1 @@ +{"mPr@50": 0.0, "mPr@60": 0.0, "mPr@70": 0.0, "mPr@80": 0.0, "mPr@90": 0.0, "mIoU": 1.03, "data_time": 0.7479451667694819, "time": 0.859009808018094} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_label_results.json b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_label_results.json new file mode 100644 index 0000000000..ae3aec8e9c --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_label_results.json @@ -0,0 +1,242 @@ +{ + "_background_": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 30.73 + }, + "apple": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "banana": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "book": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.02 + }, + "bowl": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "can": { + "Pr@50": 0.0, + "Pr@60": 0.0, + 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b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..971c6a92fa --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_r50_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,574 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/zero_shot/models/irl_vision_cat/mask2former_r50_8xb2-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.1, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=150, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 150 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=160000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'work_dirs/mask2former_r50_8xb2-160k_ade20k-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=5000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + max_size=2048, + resize_type='ResizeShortestEdge', + scales=[ + 256, + 307, + 358, + 409, + 460, + 512, + 563, + 614, + 665, + 716, + 768, + 819, + 870, + 921, + 972, + 1024, + ], + type='RandomChoiceResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + max_size=2048, + resize_type='ResizeShortestEdge', + scales=[ + 256, + 307, + 358, + 409, + 460, + 512, + 563, + 614, + 665, + 716, + 768, + 819, + 870, + 921, + 972, + 1024, + ], + type='RandomChoiceResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir='work_dirs/mask2former_r50_8xb2-160k_ade20k-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'work_dirs/mask2former_r50_8xb2-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_results.json b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_results.json new file mode 100644 index 0000000000..500f9f068a --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 0.0, + "mPr@60": 0.0, + "mPr@70": 0.0, + "mPr@80": 0.0, + "mPr@90": 0.0, + "mIoU": 1.03 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/mask2former_r50_8xb2-160k_ade20k-512x512/show/vis_data/config.py b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/mask2former_r50_8xb2-160k_ade20k-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..971c6a92fa --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/mask2former_r50_8xb2-160k_ade20k-512x512/show/vis_data/config.py @@ -0,0 +1,574 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/zero_shot/models/irl_vision_cat/mask2former_r50_8xb2-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.1, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=150, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 150 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=160000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'work_dirs/mask2former_r50_8xb2-160k_ade20k-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=5000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + max_size=2048, + resize_type='ResizeShortestEdge', + scales=[ + 256, + 307, + 358, + 409, + 460, + 512, + 563, + 614, + 665, + 716, + 768, + 819, + 870, + 921, + 972, + 1024, + ], + type='RandomChoiceResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + max_size=2048, + resize_type='ResizeShortestEdge', + scales=[ + 256, + 307, + 358, + 409, + 460, + 512, + 563, + 614, + 665, + 716, + 768, + 819, + 870, + 921, + 972, + 1024, + ], + type='RandomChoiceResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir='work_dirs/mask2former_r50_8xb2-160k_ade20k-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'work_dirs/mask2former_r50_8xb2-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/20241029_090139/20241029_090139.json b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/20241029_090139/20241029_090139.json new file mode 100644 index 0000000000..367d805493 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/20241029_090139/20241029_090139.json @@ -0,0 +1 @@ +{"mPr@50": 0.0, "mPr@60": 0.0, "mPr@70": 0.0, "mPr@80": 0.0, "mPr@90": 0.0, "mIoU": 1.02, "data_time": 0.7373388579913548, "time": 0.8207325793447948} \ No newline at end of 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a/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_results.json b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_results.json new file mode 100644 index 0000000000..d0e1c5abfe --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 0.0, + "mPr@60": 0.0, + "mPr@70": 0.0, + "mPr@80": 0.0, + "mPr@90": 0.0, + "mIoU": 1.02 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py new file mode 100644 index 0000000000..2aa724d883 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py @@ -0,0 +1,638 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/zero_shot/models/irl_vision_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=224, + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.1, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=150, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 150 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'work_dirs/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'work_dirs/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'work_dirs/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/show/vis_data/config.py b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..2aa724d883 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/show/vis_data/config.py @@ -0,0 +1,638 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/zero_shot/models/irl_vision_cat/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=224, + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.1, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=150, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 150 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'work_dirs/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'work_dirs/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'work_dirs/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/20241029_090259/20241029_090259.json b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/20241029_090259/20241029_090259.json new file mode 100644 index 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'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/zero_shot/models/irl_vision_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'work_dirs/segformer_mit-b0_8xb2-160k_ade20k-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir='work_dirs/segformer_mit-b0_8xb2-160k_ade20k-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'work_dirs/segformer_mit-b0_8xb2-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_results.json b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_results.json new file mode 100644 index 0000000000..67e5a2aad3 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 0.37, + "mPr@60": 0.0, + "mPr@70": 0.0, + "mPr@80": 0.0, + "mPr@90": 0.0, + "mIoU": 1.42 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/show/vis_data/config.py b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..ce87acd096 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/show/vis_data/config.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/zero_shot/models/irl_vision_cat/segformer_mit-b0_8xb2-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'work_dirs/segformer_mit-b0_8xb2-160k_ade20k-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir='work_dirs/segformer_mit-b0_8xb2-160k_ade20k-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'work_dirs/segformer_mit-b0_8xb2-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/20241029_090416/20241029_090416.json b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/20241029_090416/20241029_090416.json new file mode 100644 index 0000000000..169b9f1de0 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/20241029_090416/20241029_090416.json @@ -0,0 +1 @@ +{"mPr@50": 0.37, "mPr@60": 0.37, "mPr@70": 0.37, "mPr@80": 0.0, "mPr@90": 0.0, "mIoU": 1.23, "data_time": 0.7554798012688047, "time": 0.8084656539417449} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_label_results.json b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_label_results.json new file mode 100644 index 0000000000..9a39d0ad26 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_label_results.json @@ -0,0 +1,242 @@ +{ + "_background_": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 29.38 + }, + "apple": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "banana": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "book": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.09 + }, + "bowl": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "can": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cap": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cereal": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "cup": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "flashlight": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "juice_box": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "keyboard": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "knife": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "laptop": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "lemon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "marker": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "milk": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "monitor": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "mouse": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 1.1 + }, + "mug": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "orange": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "peach": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pear": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pen": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "plate": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pringles": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "scissors": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "sponge": { + "Pr@50": 11.11, + "Pr@60": 11.11, + "Pr@70": 11.11, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 6.24 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + } +} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py new file mode 100644 index 0000000000..7df2dabe9d --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/zero_shot/models/irl_vision_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=150, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'work_dirs/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=16, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir='work_dirs/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'work_dirs/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_results.json b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_results.json new file mode 100644 index 0000000000..738c61d94f --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_results.json @@ -0,0 +1,8 @@ +{ + "mPr@50": 0.37, + "mPr@60": 0.37, + "mPr@70": 0.37, + "mPr@80": 0.0, + "mPr@90": 0.0, + "mIoU": 1.23 +} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/show/vis_data/config.py b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..7df2dabe9d --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/show/vis_data/config.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/conversion_tests/zero_shot/models/irl_vision_cat/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=150, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'work_dirs/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=16, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir='work_dirs/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'work_dirs/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/results/hots_v1/mask2former_r50_8xb2-160k_ade20k-512x512/20240919_132544/20240919_132544.json b/my_projects/conversion_tests/zero_shot/results/hots_v1/mask2former_r50_8xb2-160k_ade20k-512x512/20240919_132544/20240919_132544.json new file mode 100644 index 0000000000..94e35d7961 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/results/hots_v1/mask2former_r50_8xb2-160k_ade20k-512x512/20240919_132544/20240919_132544.json @@ -0,0 +1 @@ +{"mPr@50.0": 22.43, "mPr@60.0": 20.97, "mPr@70.0": 18.47, "mPr@80.0": 16.94, "mPr@90.0": 12.33, "mIoU": 20.28, "data_time": 1.0374569472144632, "time": 2.1473879954394173} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/results/hots_v1/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_HOTS_HOTS_CAT.py b/my_projects/conversion_tests/zero_shot/results/hots_v1/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_HOTS_HOTS_CAT.py new file mode 100644 index 0000000000..49635e0302 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/results/hots_v1/mask2former_r50_8xb2-160k_ade20k-512x512/mask2former_HOTS_HOTS_CAT.py @@ -0,0 +1,583 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/zero_shot/models/mask2former_r50_8xb2-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.1, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=150, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 150 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=160000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], + keep_results=True, + output_dataset='ADE20K', + output_dir= + 'my_projects/zero_shot/results/hots_v1/mask2former_r50_8xb2-160k_ade20k-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=5000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + max_size=2048, + resize_type='ResizeShortestEdge', + scales=[ + 256, + 307, + 358, + 409, + 460, + 512, + 563, + 614, + 665, + 716, + 768, + 819, + 870, + 921, + 972, + 1024, + ], + type='RandomChoiceResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + max_size=2048, + resize_type='ResizeShortestEdge', + scales=[ + 256, + 307, + 358, + 409, + 460, + 512, + 563, + 614, + 665, + 716, + 768, + 819, + 870, + 921, + 972, + 1024, + ], + type='RandomChoiceResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ADE20K', + save_dir= + 'my_projects/zero_shot/results/hots_v1/mask2former_r50_8xb2-160k_ade20k-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/zero_shot/results/hots_v1/mask2former_r50_8xb2-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/results/hots_v1/mask2former_r50_8xb2-160k_ade20k-512x512/show/vis_data/config.py b/my_projects/conversion_tests/zero_shot/results/hots_v1/mask2former_r50_8xb2-160k_ade20k-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..49635e0302 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/results/hots_v1/mask2former_r50_8xb2-160k_ade20k-512x512/show/vis_data/config.py @@ -0,0 +1,583 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/zero_shot/models/mask2former_r50_8xb2-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.1, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=150, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 150 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=160000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], + keep_results=True, + output_dataset='ADE20K', + output_dir= + 'my_projects/zero_shot/results/hots_v1/mask2former_r50_8xb2-160k_ade20k-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=5000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + max_size=2048, + resize_type='ResizeShortestEdge', + scales=[ + 256, + 307, + 358, + 409, + 460, + 512, + 563, + 614, + 665, + 716, + 768, + 819, + 870, + 921, + 972, + 1024, + ], + type='RandomChoiceResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + max_size=2048, + resize_type='ResizeShortestEdge', + scales=[ + 256, + 307, + 358, + 409, + 460, + 512, + 563, + 614, + 665, + 716, + 768, + 819, + 870, + 921, + 972, + 1024, + ], + type='RandomChoiceResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ADE20K', + save_dir= + 'my_projects/zero_shot/results/hots_v1/mask2former_r50_8xb2-160k_ade20k-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/zero_shot/results/hots_v1/mask2former_r50_8xb2-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/results/hots_v1/mask2former_r50_8xb2-160k_ade20k-512x512/show/vis_data/vis_image/test_kitchen_6_top_raw_0.png_1.png 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a/my_projects/conversion_tests/zero_shot/results/hots_v1/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/20240919_132631/20240919_132631.json b/my_projects/conversion_tests/zero_shot/results/hots_v1/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/20240919_132631/20240919_132631.json new file mode 100644 index 0000000000..2e2abf40db --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/results/hots_v1/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/20240919_132631/20240919_132631.json @@ -0,0 +1 @@ +{"mPr@50.0": 21.6, "mPr@60.0": 19.32, "mPr@70.0": 18.13, "mPr@80.0": 15.15, "mPr@90.0": 12.62, "mIoU": 19.7, "data_time": 1.0625456501455868, "time": 2.1681800169103287} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/results/hots_v1/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_HOTS_HOTS_CAT.py b/my_projects/conversion_tests/zero_shot/results/hots_v1/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_HOTS_HOTS_CAT.py new file mode 100644 index 0000000000..9159eafc0b --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/results/hots_v1/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/maskformer_HOTS_HOTS_CAT.py @@ -0,0 +1,646 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/zero_shot/models/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=224, + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.1, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=150, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 150 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], + keep_results=True, + output_dataset='ADE20K', + output_dir= + 'my_projects/zero_shot/results/hots_v1/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ADE20K', + save_dir= + 'my_projects/zero_shot/results/hots_v1/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/zero_shot/results/hots_v1/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/results/hots_v1/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/show/vis_data/config.py b/my_projects/conversion_tests/zero_shot/results/hots_v1/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..9159eafc0b --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/results/hots_v1/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/show/vis_data/config.py @@ -0,0 +1,646 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/zero_shot/models/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=224, + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 0.1, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=150, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 150 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], + keep_results=True, + output_dataset='ADE20K', + output_dir= + 'my_projects/zero_shot/results/hots_v1/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ADE20K', + save_dir= + 'my_projects/zero_shot/results/hots_v1/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/zero_shot/results/hots_v1/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/results/hots_v1/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/show/vis_data/vis_image/test_kitchen_6_top_raw_0.png_1.png 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b/my_projects/conversion_tests/zero_shot/results/hots_v1/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/show/vis_data/vis_image/test_table_8_top_raw_0.png_17.png new file mode 100644 index 0000000000..5095dc6d0d Binary files /dev/null and b/my_projects/conversion_tests/zero_shot/results/hots_v1/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512/show/vis_data/vis_image/test_table_8_top_raw_0.png_17.png differ diff --git a/my_projects/conversion_tests/zero_shot/results/hots_v1/segformer_mit-b0_8xb2-160k_ade20k-512x512/20240919_132722/20240919_132722.json b/my_projects/conversion_tests/zero_shot/results/hots_v1/segformer_mit-b0_8xb2-160k_ade20k-512x512/20240919_132722/20240919_132722.json new file mode 100644 index 0000000000..9fab9491d7 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/results/hots_v1/segformer_mit-b0_8xb2-160k_ade20k-512x512/20240919_132722/20240919_132722.json @@ -0,0 +1 @@ +{"mPr@50.0": 12.45, "mPr@60.0": 10.48, "mPr@70.0": 8.94, "mPr@80.0": 6.15, "mPr@90.0": 2.31, "mIoU": 11.57, "data_time": 1.0961819817038143, "time": 2.121599323609296} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/results/hots_v1/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_HOTS_HOTS_CAT.py b/my_projects/conversion_tests/zero_shot/results/hots_v1/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_HOTS_HOTS_CAT.py new file mode 100644 index 0000000000..557bdcac5e --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/results/hots_v1/segformer_mit-b0_8xb2-160k_ade20k-512x512/segformer_HOTS_HOTS_CAT.py @@ -0,0 +1,328 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/zero_shot/models/segformer_mit-b0_8xb2-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], + keep_results=True, + output_dataset='ADE20K', + output_dir= + 'my_projects/zero_shot/results/hots_v1/segformer_mit-b0_8xb2-160k_ade20k-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ADE20K', + save_dir= + 'my_projects/zero_shot/results/hots_v1/segformer_mit-b0_8xb2-160k_ade20k-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/zero_shot/results/hots_v1/segformer_mit-b0_8xb2-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/results/hots_v1/segformer_mit-b0_8xb2-160k_ade20k-512x512/show/vis_data/config.py b/my_projects/conversion_tests/zero_shot/results/hots_v1/segformer_mit-b0_8xb2-160k_ade20k-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..557bdcac5e --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/results/hots_v1/segformer_mit-b0_8xb2-160k_ade20k-512x512/show/vis_data/config.py @@ -0,0 +1,328 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/zero_shot/models/segformer_mit-b0_8xb2-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], + keep_results=True, + output_dataset='ADE20K', + output_dir= + 'my_projects/zero_shot/results/hots_v1/segformer_mit-b0_8xb2-160k_ade20k-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ADE20K', + save_dir= + 'my_projects/zero_shot/results/hots_v1/segformer_mit-b0_8xb2-160k_ade20k-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/zero_shot/results/hots_v1/segformer_mit-b0_8xb2-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/results/hots_v1/segformer_mit-b0_8xb2-160k_ade20k-512x512/show/vis_data/vis_image/test_kitchen_6_top_raw_0.png_1.png 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a/my_projects/conversion_tests/zero_shot/results/hots_v1/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/20240919_132808/20240919_132808.json b/my_projects/conversion_tests/zero_shot/results/hots_v1/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/20240919_132808/20240919_132808.json new file mode 100644 index 0000000000..82c4de21de --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/results/hots_v1/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/20240919_132808/20240919_132808.json @@ -0,0 +1 @@ +{"mPr@50.0": 16.09, "mPr@60.0": 14.07, "mPr@70.0": 12.53, "mPr@80.0": 12.53, "mPr@90.0": 10.23, "mIoU": 17.1, "data_time": 1.0316821687361772, "time": 2.037519693374634} \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/results/hots_v1/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_HOTS_HOTS_CAT.py b/my_projects/conversion_tests/zero_shot/results/hots_v1/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_HOTS_HOTS_CAT.py new file mode 100644 index 0000000000..454019ca2e --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/results/hots_v1/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_HOTS_HOTS_CAT.py @@ -0,0 +1,350 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/zero_shot/models/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=150, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], + keep_results=True, + output_dataset='ADE20K', + output_dir= + 'my_projects/zero_shot/results/hots_v1/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=16, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ADE20K', + save_dir= + 'my_projects/zero_shot/results/hots_v1/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/zero_shot/results/hots_v1/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512' diff --git a/my_projects/conversion_tests/zero_shot/results/hots_v1/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/show/vis_data/config.py b/my_projects/conversion_tests/zero_shot/results/hots_v1/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..454019ca2e --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/results/hots_v1/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/show/vis_data/config.py @@ -0,0 +1,350 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/zero_shot/models/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=150, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=160000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], + keep_results=True, + output_dataset='ADE20K', + output_dir= + 'my_projects/zero_shot/results/hots_v1/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/pred_results', + target_dataset='HOTS_CAT', + test_dataset='HOTS', + type='CustomIoUMetricConversion') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=16, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='/media/ids/Ubuntu files/data/ADEChallengeData2016/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=4, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + output_dataset='ADE20K', + save_dir= + 'my_projects/zero_shot/results/hots_v1/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/show', + target_dataset='HOTS_CAT', + test_dataset='HOTS', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/zero_shot/results/hots_v1/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512' diff --git 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and b/my_projects/conversion_tests/zero_shot/results/hots_v1/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/show/vis_data/vis_image/test_table_8_top_raw_0.png_17.png differ diff --git a/my_projects/conversion_tests/zero_shot/scripts/make_zero_shot_dirs.py b/my_projects/conversion_tests/zero_shot/scripts/make_zero_shot_dirs.py new file mode 100644 index 0000000000..cb15fbc4e3 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/scripts/make_zero_shot_dirs.py @@ -0,0 +1,79 @@ +import os +import subprocess +from my_projects.scripts.parse_model_data import get_all_model_dicts +from copy import deepcopy +import shutil +project_list = [ + "bisenetv1", + "mask2former", + "maskformer", + "segformer", + "segnext" +] + +model_dicts = get_all_model_dicts(project_list=project_list) +for model_dict in model_dicts: + print("-" * 80) + for key, val in model_dict.items(): + print(f"{key} : {val}") + print() +print(len(model_dicts)) +# just the ones tested (drop other backbones) + +relevant = [] +for method in os.listdir("my_projects/best_models/selection"): + # only once + if "hots-v1" in method: + continue + [name, backbone] = method.split("_")[:2] + relevant.append(f"{name}_{backbone}") + +model_dicts_, model_dicts = model_dicts, [] +for model_dict in model_dicts_: + if model_dict['Backbone'] == "R-18-D32 (4x8)": + continue + for method in relevant: + if method in model_dict['config']: + model_dicts.append(model_dict) + +for model_dict in model_dicts: + print("-" * 80) + for key, val in model_dict.items(): + print(f"{key} : {val}") + print() +print(len(model_dicts)) + +for model_dict in model_dicts: + cfg_src_path = model_dict['config'] + _, cfg_name = os.path.split(cfg_src_path) + method_name = cfg_name.replace(".py", "") + method_path = os.path.join( + "my_projects/best_models/selection_trained", + method_name + ) + if not os.path.exists(method_path): + os.mkdir(method_path) + cfg_target_path = os.path.join( + method_path, + cfg_name + ) + shutil.copyfile(cfg_src_path, cfg_target_path) + mim_cmd = f"mim download mmseg --config {method_name} --dest {method_path}" + # # call_list = ["conda", "activate", "openmmlab", "&&"] + call_list = [] + for cmd in mim_cmd.split(): + call_list.append(cmd) + subprocess.call(call_list) + weights = [ + file_name for file_name in os.listdir(method_path) + if ".pth" in file_name + ][0] + weights = os.path.join(method_path, weights) + os.rename( + weights, + os.path.join( + method_path, + "weights.pth" + ) + ) + \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/scripts/test_ade_preds.py b/my_projects/conversion_tests/zero_shot/scripts/test_ade_preds.py new file mode 100644 index 0000000000..37ac96686c --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/scripts/test_ade_preds.py @@ -0,0 +1,45 @@ +from mmengine import Config +from mmseg.apis import init_model, inference_model +from mmseg.visualization import SegLocalVisualizer +from mmseg.utils import ade_classes, ade_palette +from matplotlib import pyplot as plt +import numpy as np +from PIL import Image +cfg_path = "my_projects/conversion_tests/zero_shot/models/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py" +config = Config.fromfile(cfg_path) + +checkpoint_path = "my_projects/conversion_tests/zero_shot/models/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512/weights.pth" + +model = init_model( + config=config, + checkpoint=checkpoint_path +) +img_1_pth = "/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/img_dir/test/mix_2_top_raw_6.png" +img_2_pth = "/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/img_dir/test/office_5_top_raw_2.png" + +img_1 = Image.open(img_1_pth) +img_2 = Image.open(img_2_pth) +vis = SegLocalVisualizer( + classes=ade_classes(), + palette=ade_palette() +) +out1 = inference_model( + model=model, + img=img_1_pth +) + +# out2 = inference_model( +# model=model, +# img=img_2 +# ) + +mask1 = vis._draw_sem_seg( + image=np.asarray(img_1.copy()), + sem_seg=out1.pred_sem_seg, + classes=ade_classes(), + palette=ade_palette(), + with_labels=True +) + +plt.imshow(mask1) +plt.show() \ No newline at end of file diff --git a/my_projects/conversion_tests/zero_shot/scripts/zero_shot_experiment.py b/my_projects/conversion_tests/zero_shot/scripts/zero_shot_experiment.py new file mode 100644 index 0000000000..15dd613ab3 --- /dev/null +++ b/my_projects/conversion_tests/zero_shot/scripts/zero_shot_experiment.py @@ -0,0 +1,259 @@ +from mmengine import Config +import argparse +import os +import subprocess +from my_projects.conversion_tests.converters.conversion_dicts import( + DATASET_CLASSES, + DATASET_PALETTE +) +from tools.test_selection import run_performance_test + + +TEST_DATASET_TEMPLATE_PATHS = { + "HOTS" : "my_projects/zero_shot/dataset_template_configs/hots_test_data.py", + "IRL_VISION" : "my_projects/zero_shot/dataset_template_configs/irl_test_data.py" +} + +TEST_DATASET_RESULTS_DIR_NAME = { + "HOTS" : "hots_v1", + "IRL_VISION" : "irl_vision" +} + +def arg_parse(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--models_path", + type=str, + default="my_projects/zero_shot/models" + ) + parser.add_argument( + '--test_dataset', + '-test_ds', + type=str, + default="HOTS", + choices=["HOTS", "IRL_VISION"] + ) + parser.add_argument( + '--output_dataset', + '-out_ds', + type=str, + default="ADE20K", + choices=["HOTS", "IRL_VISION", "ADE20K"] + ) + parser.add_argument( + '--target_dataset', + '-tar_ds', + type=str, + default="HOTS_CAT", + choices=["HOTS_CAT", "IRL_VISION_CAT"] + ) + parser.add_argument( + '--model_name', + '-mn', + type=str, + default=None, + choices=[ + 'bisenetv1', + 'mask2former', + 'maskformer', + 'segformer', + 'segnext', + None + ] + ) + parser.add_argument( + '--test_results_path', + type=str, + default="my_projects/zero_shot/results" + ) + parser.add_argument( + '--gen_new_cfg', + '-n_cfg', + action='store_true' + ) + + parser.add_argument( + '--cfg_save_dir', + '-cfg_dir', + type=str, + default="temp" + ) + + args = parser.parse_args() + return args + +def conversion_test_model( + model_path: str, + cfg_path: str, + test_results_path: str +): + model_name = get_model_dir_name(model_path=model_path) + + checkpoint_path = get_checkpoint_path(model_path=model_path) + work_dir_path = os.path.join( + test_results_path, + model_name + ) + + prediction_result_path = os.path.join( + test_results_path, + model_name, + "pred_results" + ) + + show_dir_path = os.path.join( + test_results_path, + model_name, + "show" + ) + run_performance_test( + cfg_path=cfg_path, + checkpoint_path=checkpoint_path, + work_dir_path=work_dir_path, + prediction_result_path=prediction_result_path, + show_dir_path=show_dir_path + ) + +def conversion_tests_models(args): + for model_dir_name in os.listdir(args.models_path): + if args.test_dataset.lower() not in model_dir_name.split("_")[-1]: + print(f"invalid match") + print(f"test_set: {args.test_dataset}\nmodel_dir: {model_dir_name}") + continue + model_path = os.path.join( + args.models_path, + model_dir_name + ) + + cfg_path = get_cfg_path( + model_path=os.path.join( + model_path, + "temp" + ) + ) + if not ( + os.path.exists(cfg_path) + and + args.test_dataset in cfg_path + and + args.target_dataset in cfg_path + and + not args.gen_new_cfg + ): + + cfg_path = gen_cfg(model_path=model_path, args=args) + + test_results_path = args.test_results_path + test_dataset_dir = TEST_DATASET_RESULTS_DIR_NAME[args.test_dataset] + if test_dataset_dir not in test_results_path: + test_results_path = os.path.join( + args.test_results_path, + TEST_DATASET_RESULTS_DIR_NAME[args.test_dataset] + ) + conversion_test_model( + model_path=model_path, + cfg_path=cfg_path, + test_results_path=test_results_path + ) + + +def get_cfg_path(model_path: str) -> str: + cfg_path = [ + file_name for file_name in os.listdir(model_path) + if ".py" in file_name + ] + + if cfg_path: + return os.path.join( + model_path, + cfg_path[0] + ) + return "" + +def get_checkpoint_path(model_path: str) -> str: + return os.path.join( + model_path, + "weights.pth" + ) + +def get_model_dir_name(model_path: str): + idx_ = -2 if model_path[-1] == '/' else -1 + return model_path.split("/")[idx_] + +def gen_cfg(model_path: str, args): + temp_path = os.path.join(model_path, args.cfg_save_dir) + if not os.path.exists(temp_path): + os.mkdir(temp_path) + cfg_path = get_cfg_path(model_path=model_path) + cfg = Config.fromfile(cfg_path) + ds_template_cfg = Config.fromfile( + TEST_DATASET_TEMPLATE_PATHS[args.test_dataset] + ) + cfg.merge_from_dict(ds_template_cfg.to_dict()) + cfg.merge_from_dict( + options=dict( + test_evaluator = dict( + type="CustomIoUMetricConversion", + test_dataset=args.test_dataset, + output_dataset=args.output_dataset, + target_dataset=args.target_dataset + ) + ) + ) + + cfg.merge_from_dict( + options=dict( + visualizer = dict( + name='visualizer', + type='SegLocalVisualizerConversion', + vis_backends=[ + dict(type='LocalVisBackend'), + ], + test_dataset=args.test_dataset, + output_dataset=args.output_dataset, + target_dataset=args.target_dataset + ) + ) + ) + model_name = get_model_dir_name(model_path=model_path).split("_")[0] + dump_path = os.path.join( + temp_path, + f"{model_name}_{args.test_dataset}_{args.target_dataset}.py" + ) + cfg.dump(dump_path) + return dump_path + + +def main(): + args = arg_parse() + + if args.model_name is None: + conversion_tests_models(args=args) + + else: + model_name = [ + f_name for f_name in os.listdir(args.models_path) + if args.model_name in f_name + ][0] + model_path = os.path.join( + args.models_path, + model_name + ) + try: + cfg_path = get_cfg_path( + model_path=os.path.join( + model_path, + "temp" + ) + ) + except: + cfg_path = gen_cfg(model_path=model_path, args=args) + conversion_test_model( + model_path=model_path, + cfg_path=cfg_path, + test_results_path=args.test_results_path + ) + +if __name__ == '__main__': + main() + \ No newline at end of file diff --git a/my_projects/scripts/all_per_label_results.bat b/my_projects/scripts/all_per_label_results.bat new file mode 100644 index 0000000000..0cf4f7f22d --- /dev/null +++ b/my_projects/scripts/all_per_label_results.bat @@ -0,0 +1,20 @@ +python my_projects/scripts/plot_per_label_results.py "my_projects/test_results/arid10_cat/" +python my_projects/scripts/plot_per_label_results.py "my_projects/test_results/arid20_cat/" + +python my_projects/scripts/plot_per_label_results.py "my_projects/test_results/hots_v1/" +python my_projects/scripts/plot_per_label_results.py "my_projects/test_results/hots_v1_cat/" + +python my_projects/scripts/plot_per_label_results.py "my_projects/test_results/irl_vision/" +python my_projects/scripts/plot_per_label_results.py "my_projects/test_results/irl_vision_cat/" + + + +python my_projects/scripts/plot_per_label_results.py "my_projects/conversion_tests/test_results/arid202arid10" +python my_projects/scripts/plot_per_label_results.py "my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat" + +python my_projects/scripts/plot_per_label_results.py "my_projects/conversion_tests/test_results/hots2hots_cat" +python my_projects/scripts/plot_per_label_results.py "my_projects/conversion_tests/test_results/hots2irl_vision_cat" + +python my_projects/scripts/plot_per_label_results.py "my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat" +python my_projects/scripts/plot_per_label_results.py "my_projects/conversion_tests/test_results/irl_vision2hots_cat" +python my_projects/scripts/plot_per_label_results.py "my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat" \ No newline at end of file diff --git a/my_projects/scripts/all_pred_imgs.bat b/my_projects/scripts/all_pred_imgs.bat new file mode 100644 index 0000000000..b6cd1b4f56 --- /dev/null +++ b/my_projects/scripts/all_pred_imgs.bat @@ -0,0 +1,29 @@ +python my_projects/scripts/visualize_preds.py my_projects/test_results/hots_v1/ hots -raw -gt -sn HOTS_set +python my_projects/scripts/visualize_preds.py my_projects/test_results/hots_v1_cat/ hots-c -raw -gt -sn HOTS-C_set +python my_projects/scripts/visualize_preds.py my_projects/test_results/irl_vision/ irl_vision -raw -gt -sn SOD_set +python my_projects/scripts/visualize_preds.py my_projects/test_results/irl_vision_cat/ irl_vision_cat -raw -gt -sn SOD-C_set +python my_projects/scripts/visualize_preds.py my_projects/test_results/arid20_cat/ ARID20 -raw -gt -sn ARID20_set +python my_projects/scripts/visualize_preds.py my_projects/test_results/arid10_cat/ ARID10 -raw -gt -sn ARID10_set + +python my_projects/scripts/visualize_preds.py my_projects/test_results/arid20_cat/ ARID20 -raw -gt -sn ARID20_clutter_set -cl + + +python my_projects/scripts/visualize_preds.py my_projects/conversion_tests/test_results/hots2hots_cat HOTS -raw -gt -sn HOTS2HOTS-C -ods HOTS -tds HOTS-C + +python my_projects/scripts/visualize_preds.py my_projects/conversion_tests/test_results/irl_vision2irl_vision_cat SOD -raw -gt -sn SOD2SOD-C -ods SOD -tds SOD-C + + + +python my_projects/scripts/visualize_preds.py my_projects/conversion_tests/test_results/hots_cat2irl_vision_cat HOTS-C -raw -gt -sn HOTS-C2SOD-C -ods HOTS-C -tds SOD-C + +python my_projects/scripts/visualize_preds.py my_projects/conversion_tests/test_results/irl_vision_cat2hots_cat SOD-C -raw -gt -sn SOD-C2HOT-C -ods SOD-C -tds HOTS-C + +python my_projects/scripts/visualize_preds.py my_projects/conversion_tests/test_results/arid202arid10 ARID20 -raw -gt -sn ARID20_ARID10 -ods ARID20 -tds ARID10 + +python my_projects/scripts/visualize_preds.py my_projects/test_results/sodhots-c/ sodhots-c -raw -gt -sn SODHOTS-C_set + + + +python my_projects/scripts/visualize_preds.py my_projects/conversion_tests/zero_shot/qualitative_results/hots_cat ADE20K -raw -gt -sn zero_shot_HOTS-C -ods ADE20K -tds HOTS-C + +python my_projects/scripts/visualize_preds.py my_projects/conversion_tests/zero_shot/qualitative_results/irl_vision_cat ADE20K -raw -gt -sn zero_shot_SOD-C -ods ADE20K -tds SOD-C \ No newline at end of file diff --git a/my_projects/scripts/analyze_logs.py b/my_projects/scripts/analyze_logs.py new file mode 100644 index 0000000000..2d199e1efe --- /dev/null +++ b/my_projects/scripts/analyze_logs.py @@ -0,0 +1,167 @@ +# Copyright (c) OpenMMLab. All rights reserved. +"""Modified from https://github.com/open- +mmlab/mmdetection/blob/master/tools/analysis_tools/analyze_logs.py.""" +import argparse +import json +from collections import defaultdict +import my_projects.scripts.plotting_utils as p_utils +import matplotlib.pyplot as plt +from matplotlib import rcParams, rcParamsDefault +import seaborn as sns +import numpy as np + +def reduce_zeros(iteration): + if iteration >= 1000: + return str( + int( + iteration / 1000 + ) + ) + "k" + return iteration + +def plot_curve(log_dicts, args): + if args.backend is not None: + plt.switch_backend(args.backend) + p_utils.set_params(param_dict=p_utils.LEARNING_PLOT_PARAMS) + # if legend is None, use {filename}_{key} as legend + legend = args.legend + if legend is None: + legend = [] + for json_log in args.json_logs: + for metric in args.keys: + legend.append(f'{json_log}_{metric}') + assert len(legend) == (len(args.json_logs) * len(args.keys)) + metrics = args.keys + + num_metrics = len(metrics) + for i, log_dict in enumerate(log_dicts): + epochs = list(log_dict.keys()) + for j, metric in enumerate(metrics): + print(f'plot curve of {args.json_logs[i]}, metric is {metric}') + plot_epochs = [] + plot_iters = [] + plot_values = [] + # In some log files exist lines of validation, + # `mode` list is used to only collect iter number + # of training line. + for epoch in epochs: + epoch_logs = log_dict[epoch] + if metric not in epoch_logs.keys(): + continue + if metric in ['mIoU', 'mAcc', 'aAcc']: + plot_epochs.append(epoch) + plot_values.append(epoch_logs[metric][0]) + else: + for idx in range(len(epoch_logs[metric])): + plot_iters.append(epoch_logs['step'][idx]) + plot_values.append(epoch_logs[metric][idx]) + ax = plt.gca() + label = legend[i * num_metrics + j] + if metric in ['mIoU', 'mAcc', 'aAcc']: + + plt.xlabel('step') + plt.plot(plot_epochs, plot_values, label=label) + plot_epochs_reduced = plot_epochs + if args.reduce_zeros: + plot_epochs_reduced = [ + reduce_zeros(epoch) for epoch in plot_epochs + ] + ax.set_xticks(plot_epochs, plot_epochs_reduced) + else: + plt.xlabel('iter') + plt.plot(plot_iters, plot_values, label=label) + plt.legend(loc='lower right') + if args.title is not None: + plt.title(args.title) + if args.out is None: + plt.show() + else: + print(f'save curve to: {args.out}') + plt.yticks(np.arange(0, 110, 10)) + plt.ylabel("mIoU") + plt.ylim(0, 101) + plt.tight_layout() + plt.savefig(args.out) + plt.cla() + p_utils.reset_params() + + +def parse_args(): + parser = argparse.ArgumentParser(description='Analyze Json Log') + parser.add_argument( + 'json_logs', + type=str, + nargs='+', + help='path of train log in json format') + parser.add_argument( + '--keys', + type=str, + nargs='+', + default=['mIoU'], + help='the metric that you want to plot') + parser.add_argument('--title', type=str, help='title of figure') + parser.add_argument( + '--legend', + type=str, + nargs='+', + default=None, + help='legend of each plot') + parser.add_argument( + '--backend', type=str, default=None, help='backend of plt') + parser.add_argument( + '--style', type=str, default='dark', help='style of plt') + parser.add_argument('--out', type=str, default=None) + parser.add_argument( + '--reduce_zeros', + '-rz', + action='store_true' + ) + args = parser.parse_args() + return args + + +def load_json_logs(json_logs): + # load and convert json_logs to log_dict, key is step, value is a sub dict + # keys of sub dict is different metrics + # value of sub dict is a list of corresponding values of all iterations + log_dicts = [dict() for _ in json_logs] + prev_step = 0 + for json_log, log_dict in zip(json_logs, log_dicts): + with open(json_log) as log_file: + for line in log_file: + log = json.loads(line.strip()) + # the final step in json file is 0. + if 'step' in log and log['step'] != 0: + step = log['step'] + prev_step = step + else: + step = prev_step + if step not in log_dict: + log_dict[step] = defaultdict(list) + for k, v in log.items(): + log_dict[step][k].append(v) + return log_dicts + + +def main(): + rcParams.update( + { + 'figure.figsize' : (12, 10), + 'legend.fontsize' : 30, + 'axes.labelsize' : 30, + 'axes.titlesize' : 30, + 'xtick.labelsize' : 30, + 'ytick.labelsize' : 30, + 'lines.linewidth' : 6 + } + ) + args = parse_args() + json_logs = args.json_logs + for json_log in json_logs: + assert json_log.endswith('.json') + log_dicts = load_json_logs(json_logs) + plot_curve(log_dicts, args) + rcParams.update(rcParamsDefault) + +if __name__ == '__main__': + main() diff --git a/my_projects/scripts/assess_global_performance.py b/my_projects/scripts/assess_global_performance.py new file mode 100644 index 0000000000..7554ab646f --- /dev/null +++ b/my_projects/scripts/assess_global_performance.py @@ -0,0 +1,899 @@ +import argparse +import os +from copy import deepcopy +from clean_json_for_reporting import ( + load_json_file, + save_dict_as_json, + fix_jsons, + gen_default_save_path +) +from plotting_utils import ( + map_metric_key, + map_metric_key_strict +) + +import numpy as np +from matplotlib import pyplot as plt +import matplotlib.lines as mlines +import plotting_utils as p_utils + +KEYS_OF_INTEREST = [ + "mPr@50", + "mPr@60", + "mPr@70", + "mPr@80", + "mPr@90", + "mIoU", + "FPS", + "Mem (MB)" +] + + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + '--results_path', + '-rp', + help='dir containing model dirs', + type=str, + default="my_projects/test_results" + ) + parser.add_argument( + '--save_app', + '-sa', + type=str, + help='save file appendix', + default="" + ) + parser.add_argument( + '--save_path', + '-sp', + type=str, + default="my_projects/images_plots/acc_eff_trade_off/" + ) + parser.add_argument( + '--fix_jsons', + '-fj', + action='store_true' + ) + parser.add_argument( + '--scoring_mode', + '-sm', + type=str, + choices=["RANK", "PROP", "ALL", "NORM"], + default="ALL" + ) + + parser.add_argument( + '--plot_data', + '-plt', + action='store_true' + ) + parser.add_argument( + '--plot_th_analysis', + '-plt_th', + action='store_true' + ) + parser.add_argument( + '--verbose', + '-v', + action='store_true' + ) + args = parser.parse_args() + + return args + +def score_model_on_metrics_by_rank( + model_score_dict: dict, + dataset_dict: dict, + metric: str +) -> dict: + descending_ = descending_order(metric=metric) + sort_dict = dict( + sorted( + dataset_dict.items(), + key=lambda d : d[1][metric], + reverse=descending_ + ) + ) + + for model_rank, (model_name, _) in enumerate(sort_dict.items()): + n_models = len(dataset_dict.keys()) + model_score = n_models - model_rank + model_score_dict[model_name][metric] += model_score + # print(f"rank, name, score: {model_rank}, {model_name}, {model_data[metric]}") + return model_score_dict + +def score_on_metric_by_proportion( + model_score_dict: dict, + dataset_dict: dict, + metric: str +) -> dict: + metric_vector = [ + metric_dict[metric] + for model_name, metric_dict in dataset_dict.items() + ] + metric_mean = np.mean(metric_vector) + metric_std = np.std(metric_vector) + modifier = 1 if descending_order(metric=metric) else -1 + for model_name, metric_dict in dataset_dict.items(): + model_metric_score = metric_dict[metric] + model_metric_score = modifier * (model_metric_score - metric_mean) + model_metric_score /= metric_std + + model_score_dict[model_name][metric] += model_metric_score + return model_score_dict + +def score_on_metric_normalized( + model_score_dict: dict, + dataset_dict: dict, + metric: str +) -> dict: + metric_vector = [ + metric_dict[metric] + for model_name, metric_dict in dataset_dict.items() + ] + if not descending_order(metric=metric): + metric_vector = [ + 1 / metric_dict[metric] + for model_name, metric_dict in dataset_dict.items() + ] + metric_total = sum(metric_vector) + for model_idx, (model_name, metric_dict) in enumerate(dataset_dict.items()): + model_metric_score = metric_vector[model_idx] / metric_total + model_score_dict[model_name][metric] += model_metric_score + return model_score_dict + + + +SCORING_MODE = { + "RANK" : score_model_on_metrics_by_rank, + "PROP" : score_on_metric_by_proportion, + "NORM" : score_on_metric_normalized +} + +def score_model_on_metric( + model_score_dict: dict, + dataset_dict: dict, + metric: str, + scoring_mode = "NORM" +) -> dict: + return SCORING_MODE[scoring_mode]( + model_score_dict=model_score_dict, + dataset_dict=dataset_dict, + metric=metric + ) +def compress_precision_scores_per_model( + global_score_per_model: dict +) -> dict: + # compressed = {} + # return compressed + pass + +def calc_efficiency_scores_per_model( + global_score_per_model: dict +) -> dict: + # score_dict = {} + # return score_dict + pass + +def calc_total_score_with_metric_weights( + global_score_per_model: dict, + weights: dict, + ignore_unweighted = True +) -> dict: + per_model_total_score = {} + for model_name, metric_score_dict in global_score_per_model.items(): + per_model_total_score[model_name] = 0 + for metric_name, metric_score in metric_score_dict.items(): + if metric_name in weights.keys(): + weighted_val = metric_score * weights[metric_name] + per_model_total_score[model_name] += weighted_val + elif not ignore_unweighted: + per_model_total_score[model_name] += metric_score + return per_model_total_score + +# returns if higher is better +def descending_order(metric): + return metric != "Mem (MB)" + +def fix_data_dataset( + args, + dataset_dir_name +): + + json_results_path = os.path.join( + args.results_path, + dataset_dir_name, + "data", + f"{dataset_dir_name}_results.json" + ) + fix_jsons( + global_results_json_path=json_results_path, + global_results_json_save_path=gen_default_save_path( + source_json_path=json_results_path, + save_app=args.save_app + ) + ) + +def fix_results_jsons_all_datasets(args): + for dataset_dir_name in os.listdir(args.results_path): + fix_data_dataset( + args=args, + dataset_dir_name=dataset_dir_name + ) + json_results_path = os.path.join( + args.results_path, + dataset_dir_name, + "data", + f"{dataset_dir_name}_results.json" + ) + dataset_results_dict = load_json_file(json_file_path=json_results_path) + dataset_results_dict = filter_dataset_results_keys( + dataset_results_dict=dataset_results_dict + ) + save_path = gen_default_save_path( + source_json_path=json_results_path, + save_app=args.save_app + ) + save_dict_as_json( + data_dict=dataset_results_dict, + dump_file_path=save_path + ) + +def filter_dataset_results_keys(dataset_results_dict: dict) -> dict: + new_dict = {} + for model_name, metric_dict in dataset_results_dict.items(): + new_dict[model_name] = { + map_metric_key(metric_key) : metric_val + for metric_key, metric_val in metric_dict.items() + if map_metric_key(metric_key) in KEYS_OF_INTEREST + } + return new_dict + +def collect_all_datasets_results_jsons(args) -> dict: + global_dict = {} + for dataset_dir_name in os.listdir(args.results_path): + json_results_path = os.path.join( + args.results_path, + dataset_dir_name, + "data", + f"{dataset_dir_name}_results.json" + ) + dataset_results_dict = load_json_file(json_file_path=json_results_path) + global_dict[dataset_dir_name] = dataset_results_dict + return global_dict + +def get_model_score_on_dataset( + dataset_dict: dict, + scoring_mode = "RANK" +) -> dict: + model_score_dict = { + model_name : { + metric_name : 0 for metric_name in KEYS_OF_INTEREST + } for model_name in dataset_dict.keys() + } + for metric in KEYS_OF_INTEREST: + model_score_dict = score_model_on_metric( + model_score_dict=model_score_dict, + dataset_dict=dataset_dict, + metric=metric, + scoring_mode=scoring_mode + ) + return model_score_dict + +def merge_model_score_dicts( + ms_dict_0: dict, + ms_dict_1: dict = None +) -> dict: + if not ms_dict_1: + ms_dict_1 = { + model_name : { + metric_name : 0 for metric_name in KEYS_OF_INTEREST + } for model_name in ms_dict_0.keys() + } + new_ms_dict = {} + model_names = [ + key for key in ms_dict_0.keys() + if key in ms_dict_1.keys() + ] + if ( + len(model_names) < len(ms_dict_0.keys()) + or + len(model_names) < len(ms_dict_1.keys()) + or + len(ms_dict_0.keys()) != len(ms_dict_1.keys()) + ): + print("error") + exit() + for model_name in model_names: + new_ms_dict[model_name] = {} + for metric_name in KEYS_OF_INTEREST: + val_0 = ms_dict_0[model_name][metric_name] + val_1 = ms_dict_1[model_name][metric_name] + new_ms_dict[model_name][metric_name] = val_0 + val_1 + return new_ms_dict + + + +def generate_scatter_data( + global_dict: dict, + x_key, + y_key, + area_key = None, + mark_models = True +): + # TODO add params + x_data = [] + y_data = [] + area_data = [] + color_data = [] + for dataset_name, dataset_dict in global_dict.items(): + for model_name, model_data in dataset_dict.items(): + x_data.append(model_data[x_key]) + y_data.append(model_data[y_key]) + if area_key: + area_data.append(model_data[area_key]) + if mark_models: + color_data.append(p_utils.MODEL_COLORS[model_name]) + return x_data, y_data, area_data, color_data + +def make_handles(): + legend_handles = [] + for model_name, color in p_utils.MODEL_COLORS.items(): + + legend_handles.append( + mlines.Line2D( + [], [], + color=color, + label=model_name, + linestyle='-', + linewidth=6 + ) + ) + return legend_handles + +def plot_trade_off_global( + global_dict: dict, + x_key, + y_key, + area_key = None, + mark_models = True, + save_path = None +): + + x_data, y_data, area_data, color_data = generate_scatter_data( + global_dict=global_dict, + x_key=x_key, + y_key=y_key, + area_key=area_key, + mark_models=mark_models + ) + + if not area_data: + area_data = None + if not color_data: + color_data = None + area_data = np.asarray(area_data) * 2 + p_utils.set_params(param_dict=p_utils.TRADEOFF_PLOT_PARAMS) + plt.scatter( + x=x_data, + y=y_data, + s=area_data, + c=color_data + ) + if color_data: + plt.legend(handles=make_handles(), + bbox_to_anchor=(-0.02, 0.98, 1.05, 0.25), + ncol=3, + mode="expand" + ) + plt.xlabel(xlabel=x_key) + plt.ylabel(ylabel=y_key) + plt.xlim(70, 94) + if save_path: + print(f"saved fig in : {save_path}") + plt.savefig( + save_path, + dpi=100, + bbox_inches='tight' + ) + else: + plt.show() + p_utils.reset_params() + + +def collect_score_per_model_over_all_datasets( + global_dict: dict, + scoring_mode = "RANK" +) -> tuple: + total_model_score_dict = {} + per_dataset_score = {} + for dataset_name, dataset_dict in global_dict.items(): + model_score_dict = get_model_score_on_dataset( + dataset_dict=dataset_dict, + scoring_mode=scoring_mode + ) + per_dataset_score[dataset_name] = model_score_dict + total_model_score_dict = merge_model_score_dicts( + ms_dict_0=model_score_dict, + ms_dict_1=total_model_score_dict + ) + return total_model_score_dict, per_dataset_score + + + +def analyse_weights( + total_model_score_dict: dict, + labeled_custom_weights: dict, + verbose: bool = False +): + model_weight_data = {} + for label, custom_weights in labeled_custom_weights.items(): + + + model_score_global = calc_total_score_with_metric_weights( + global_score_per_model=total_model_score_dict, + weights=custom_weights + ) + model_score_global = dict( + sorted( + model_score_global.items(), + key=lambda d: d[1], + reverse=True + ) + ) + model_weight_data[label] = model_score_global + + if verbose: + + print(f"\nweights: {label}") + for key, val in custom_weights.items(): + print(f"{key} : {val}") + print("\nResults sorted: ") + for key, val in model_score_global.items(): + print(f"{key} : {val}") + return model_weight_data + + +def random_analysis( + total_model_score_dict: dict +): + + pr_keys = [ + pr_metric for pr_metric in KEYS_OF_INTEREST + if "mPr" in pr_metric + ] + acc_weights = { + "mIoU" : 1 + } + eff_weights = { + "FPS" : 1, + "Mem (MB)" : 1 + } + fps_weights = { + "FPS" : 1 + } + mem_weights = { + "Mem (MB)" : 1 + } + sorted_pr_keys = sorted(pr_keys, reverse=True) + pr_scaled_by_th = { + pr_key : (len(sorted_pr_keys) - pr_rank) / len(sorted_pr_keys) + for pr_rank, pr_key in enumerate(sorted_pr_keys) + } + # normalize pr + pr_scaled_by_th = { + key : val / sum(pr_scaled_by_th.values()) + for key, val in pr_scaled_by_th.items() + } + + + custom_weights = {} + for key, val in pr_scaled_by_th.items(): + custom_weights[key] = val + for key, val in eff_weights.items(): + custom_weights[key] = 1 + for key, val in acc_weights.items(): + custom_weights[key] = 1 + + + labeled_weights = { + "Global" : custom_weights, + "Efficiency" : eff_weights, + "FPS" : fps_weights, + "Memory" : mem_weights, + "Precision" : pr_scaled_by_th, + "Accuracy" : acc_weights + } + + # present Props: + labeled_scores = analyse_weights( + total_model_score_dict=total_model_score_dict, + labeled_custom_weights=labeled_weights, + verbose=True + ) + +def av_results( + total_model_score_dict: dict, + n_measurements: int +): + for model_name, metric_score_dict in total_model_score_dict.items(): + for metric_name, metric_score in metric_score_dict.items(): + metric_score_dict[metric_name] /= n_measurements + + +def threshold_analyses( + total_model_score_dict: dict, + scoring_mode: str, + plot_th_analysis: bool = False, + verbose: bool = False, + save_path_dir = "my_projects/images_plots/acc_eff_trade_off" +): + if verbose: + print(f"{'#' * 80}\n THRESHOLD_ANALYSES\n") + + pr_keys = [ + pr_metric for pr_metric in KEYS_OF_INTEREST + if "mPr" in pr_metric + ] + acc_weights = { + "mIoU" : 1 + } + eff_weights = { + "FPS" : 1, + "Mem (MB)" : 1 + } + + sorted_pr_keys = sorted(pr_keys, reverse=True) + pr_scaled_by_th = { + pr_key : (len(sorted_pr_keys) - pr_rank) / len(sorted_pr_keys) + for pr_rank, pr_key in enumerate(sorted_pr_keys) + } + # normalize pr + pr_scaled_by_th = { + key : val / sum(pr_scaled_by_th.values()) + for key, val in pr_scaled_by_th.items() + } + + + default_weights = {} + for key, val in pr_scaled_by_th.items(): + default_weights[key] = val + for key, val in eff_weights.items(): + default_weights[key] = 1 + for key, val in acc_weights.items(): + default_weights[key] = 1 + + th_result_dict = {} + for metric_key in KEYS_OF_INTEREST: + if "mPr" in metric_key: + continue + factors = [ + fac for fac in np.arange(0, 10, 0.1) + ] + labeled_weights = {} + for fac in factors: + new_weights = deepcopy(default_weights) + new_weights[metric_key] = fac + labeled_weights[fac] = new_weights + labeled_scores = analyse_weights( + total_model_score_dict=total_model_score_dict, + labeled_custom_weights=labeled_weights, + verbose=False + ) + th_result_dict[metric_key] = labeled_scores + if verbose: + print_labeled_scores( + metric_key=metric_key, + default_weights=default_weights, + labeled_scores=labeled_scores + ) + if plot_th_analysis: + plot_threshold_analysis( + metric_key=metric_key, + labeled_scores=labeled_scores, + scoring_mode=scoring_mode, + save_path_dir=save_path_dir + ) + # Pr analysis + factors = [ + fac for fac in np.arange(0, 10, 0.1) + ] + labeled_weights = {} + for fac in factors: + new_weights = deepcopy(default_weights) + for pr_key in pr_keys: + new_weights[pr_key] *= fac + labeled_weights[fac] = new_weights + labeled_scores = analyse_weights( + total_model_score_dict=total_model_score_dict, + labeled_custom_weights=labeled_weights, + verbose=False + ) + th_result_dict["mPr"] = labeled_scores + if verbose: + print_labeled_scores( + metric_key="mPr", + default_weights=default_weights, + labeled_scores=labeled_scores + ) + if plot_th_analysis: + plot_threshold_analysis( + metric_key="mPr", + labeled_scores=labeled_scores, + scoring_mode=scoring_mode, + save_path_dir=save_path_dir + ) + return th_result_dict + +def plot_threshold_analysis_compact( + th_results_dict, + save_path_dir = "my_projects/images_plots/acc_eff_trade_off" +): + p_utils.set_params(param_dict=p_utils.PRED_VISUALIZATION_FIGURE_PARAMS_3ROWS) + figure = plt.figure() + subplots = figure.subplots( + nrows=3, + ncols=4, + gridspec_kw = { + 'wspace':0.01, + 'hspace':0.001 + } + ) + y_lims_row = { + 0 : (0, 50), + 1 : (-10, 10), + 2 : (0, 3) + } + score_type_row = { + 0 : "rank", + 1 : "proportional", + 2 : "normalized" + } + for row_idx, (mode, th_results) in enumerate(th_results_dict.items()): + for col_idx, (metric_key, labeled_scores) in enumerate(th_results.items()): + axes = subplots[row_idx][col_idx] + x_axis = [ + float(factor) for factor in list(labeled_scores.keys()) + ] + model_score_lists_dict = { + model_name : [] + for model_name in p_utils.MODEL_COLORS.keys() + } + for factor, model_score_dict in labeled_scores.items(): + for model_name, total_score in model_score_dict.items(): + model_score_lists_dict[model_name].append(total_score) + + for model_name, model_score_list in model_score_lists_dict.items(): + axes.plot( + x_axis, + model_score_list, + color=p_utils.MODEL_COLORS[model_name] + ) + axes.set_ylim(*y_lims_row[row_idx]) + axes.set_xlabel( + f"{metric_key} weight", + fontsize=16 + ) + axes.set_ylabel( + f"score\n{score_type_row[row_idx]}", + fontsize=16 + ) + axes.get_yaxis().set_visible( + col_idx == 0 + ) + axes.get_xaxis().set_visible( + row_idx == 2 + ) + figure.legend( + handles=make_handles(), + bbox_to_anchor=(0, 0.94, 1, 0.25), + ncol=3, + mode="expand" + ) + figure.subplots_adjust(hspace=0.01) + # figure.tight_layout() + save_path = os.path.join( + save_path_dir, + f"all_scores" + ) + + print(f"saved fig in : {save_path}") + plt.savefig( + save_path, + dpi=100, + bbox_inches='tight' + ) + plt.cla() + p_utils.reset_params() + +def plot_threshold_analysis( + metric_key, + labeled_scores, + scoring_mode, + save_path_dir = "my_projects/images_plots/acc_eff_trade_off" +): + + p_utils.set_params(param_dict=p_utils.TRADEOFF_PLOT_PARAMS) + x_axis = [ + float(factor) for factor in list(labeled_scores.keys()) + ] + model_score_lists_dict = { + model_name : [] + for model_name in p_utils.MODEL_COLORS.keys() + } + for factor, model_score_dict in labeled_scores.items(): + for model_name, total_score in model_score_dict.items(): + model_score_lists_dict[model_name].append(total_score) + + for model_name, model_score_list in model_score_lists_dict.items(): + plt.plot( + x_axis, + model_score_list, + color=p_utils.MODEL_COLORS[model_name] + ) + plt.legend( + handles=make_handles(), + bbox_to_anchor=(0, 0.90, 1, 0.02), + ncol=3, + mode="expand") + save_path = os.path.join( + save_path_dir, + f"{scoring_mode}_{metric_key}" + ) + plt.xlabel(f"{metric_key} weight") + plt.ylabel("score") + print(f"saved fig in : {save_path}") + plt.savefig( + save_path, + dpi=100, + bbox_inches='tight' + ) + plt.cla() + p_utils.reset_params() + + + +def print_labeled_scores( + metric_key, + default_weights, + labeled_scores, + max_items = 10 +): + print_every = len(labeled_scores.items()) / max_items + print(f"{'#' * 80}\n {metric_key} comparison") + print(f"\ndefault weights:") + for key, val in default_weights.items(): + print(f"{key} : {val}") + for score_idx, (label, model_score_dict) in enumerate(labeled_scores.items()): + if score_idx % print_every != 0: + continue + print(f"\n{metric_key} weight factor: {label}") + reverse = descending_order(metric=metric_key) + model_scores = dict( + sorted( + model_score_dict.items(), + key=lambda d: d[1], + reverse=reverse + ) + ) + for model_name, total_score in model_scores.items(): + print(f"{model_name} : {total_score}") + + + + +def print_per_model_score( + total_model_score_dict: dict +): + print("\nprint per model score") + for model_name, metric_dict in total_model_score_dict.items(): + print(model_name) + for metric_name, metric_value in metric_dict.items(): + print(f"{metric_name} : {metric_value}") + print() + +def get_metric_per_model(global_dict: dict): + per_model_dict = {} + for dataset_name, model_data in global_dict.items(): + for model_name, metric_data in model_data.items(): + if model_name not in per_model_dict.keys(): + per_model_dict[model_name] = { + metric_key : [] for metric_key in KEYS_OF_INTEREST + } + for metric_key, metric_value in metric_data.items(): + per_model_dict[model_name][metric_key].append(metric_value) + return per_model_dict +def main(): + args = parse_args() + if args.fix_jsons: + fix_results_jsons_all_datasets(args=args) + global_dict = collect_all_datasets_results_jsons(args=args) + + # Ignore concate dataset + global_dict = { + key : val for key, val in global_dict.items() + if key != "sodhots-c" + } + + + per_mod_dict = get_metric_per_model(global_dict=global_dict) + for model_name, metric_data in per_mod_dict.items(): + print(f"\n{model_name}: ") + for metric_name, metric_value_list in metric_data.items(): + print(f" {metric_name} : {round(np.mean(metric_value_list),1)}") + exit() + + + if args.plot_data: + save_path = os.path.join( + args.save_path, + "global_scatter" + ) + plot_trade_off_global( + global_dict=global_dict, + x_key="mIoU", + y_key="FPS", + area_key="Mem (MB)", + mark_models=True, + save_path=save_path + ) + if args.scoring_mode == "ALL": + th_results_dict = {} + for mode in SCORING_MODE.keys(): + + per_model, per_dataset = collect_score_per_model_over_all_datasets( + global_dict=global_dict, + scoring_mode=mode + ) + + av_results( + total_model_score_dict=per_model, + n_measurements=len(per_dataset.keys()) + ) + + if args.verbose: + print('#' * 80) + print(f"scoring_mode: {mode}") + print_per_model_score(total_model_score_dict=per_model) + + # random_analysis( + # total_model_score_dict=per_model + # ) + th_results_dict[mode] = threshold_analyses( + total_model_score_dict=per_model, + scoring_mode=mode, + plot_th_analysis=args.plot_th_analysis, + verbose=args.verbose, + save_path_dir=args.save_path + ) + plot_threshold_analysis_compact(th_results_dict=th_results_dict) + else: + per_model, per_dataset = collect_score_per_model_over_all_datasets( + global_dict=global_dict, + scoring_mode=args.scoring_mode + ) + av_results( + total_model_score_dict=per_model, + n_measurements=len(per_dataset.keys()) + ) + # random_analysis( + # total_model_score_dict=per_model + # ) + threshold_analyses( + total_model_score_dict=per_model, + scoring_mode=args.scoring_mode, + plot_th_analysis=args.plot_th_analysis, + verbose=args.verbose, + save_path_dir=args.save_path + ) + + + +if __name__ == '__main__': + main() + + + \ No newline at end of file diff --git a/my_projects/scripts/clean_json_for_reporting.py b/my_projects/scripts/clean_json_for_reporting.py new file mode 100644 index 0000000000..5e48cef695 --- /dev/null +++ b/my_projects/scripts/clean_json_for_reporting.py @@ -0,0 +1,356 @@ +import json +import os +import argparse +import numpy as np +from plotting_utils import ( + map_dataset_name, map_metric_key, map_model_name, map_metric_key_strict +) +map_basic2plot = { + "mPr@50.0" : "mPr@50", + "mPr@60.0" : "mPr@60", + "mPr@70.0" : "mPr@70", + "mPr@80.0" : "mPr@80", + "mPr@90.0" : "mPr@90", + "mIoU" : "mIoU", + "average_fps" : "FPS", + "average_mem" : "Mem (MB)" +} + +map_basic2latex = { + "mPr@50.0" : "mPr\textsubscript{50}", + "mPr@60.0" : "mPr\textsubscript{60}", + "mPr@70.0" : "mPr\textsubscript{70}", + "mPr@80.0" : "mPr\textsubscript{80}", + "mPr@90.0" : "mPr\textsubscript{90}", + "mIoU" : "mIoU", + "average_fps" : "FPS", + "average_mem" : "Mem (MB)" +} + +map_plot2latex = { + "mPr@50" : "mPr\textsubscript{50}", + "mPr@60" : "mPr\textsubscript{60}", + "mPr@70" : "mPr\textsubscript{70}", + "mPr@80" : "mPr\textsubscript{80}", + "mPr@90" : "mPr\textsubscript{90}", + "mIoU" : "mIoU", + "FPS" : "FPS", + "Mem (MB)" : "Mem (MB)" +} + +map_plot2basic = { + value : key for key, value in map_basic2plot.items() +} +# map_plot2latex +# for key, val in map_basic2latex.items(): +# print(f' "{key}" : "{val}",') +def save_dict_as_json(data_dict, dump_file_path): + print(f"saving to : {dump_file_path}") + with open(dump_file_path, 'w') as dump_file: + json.dump(data_dict, dump_file, indent=4) + +def load_json_file(json_file_path: str) -> dict: + with open(json_file_path, 'r') as file: + return json.load(file) + +def make_clean_performance_json_latex(data_dict, n_decimals = 1): + clean_dict = {} + for model_name, model_data in data_dict.items(): + clean_dict[model_name] = {} + for key, val in model_data.items(): + clean_key = None + if key in map_basic2latex.keys(): + clean_key = map_basic2latex[key] + elif key in map_plot2latex.keys(): + clean_key = map_plot2latex[key] + + if clean_key is not None: + clean_dict[model_name][clean_key] = round(val, n_decimals) + return clean_dict + +def make_latex_results(test_results_path = "my_projects/test_results"): + for dataset_dir in os.listdir(test_results_path): + dataset_dir_path = os.path.join(test_results_path, dataset_dir) + json_file_path = os.path.join( + dataset_dir_path, + "data", + f"{dataset_dir}_results.json" + ) + data_dict = load_json_file( + json_file_path=json_file_path + ) + latex_dict = make_clean_performance_json_latex(data_dict=data_dict) + latex_file_path = json_file_path.replace("results.json", "latex.json") + save_dict_as_json( + data_dict=latex_dict, + dump_file_path=latex_file_path + ) + +def gen_latex_class_label(class_label): + latex_label = class_label + # latex_label = f"\texttt{{{class_label}}}" + # latex_label = latex_label.replace("_", "_") + return latex_label + +def make_clean_confusion_json_latex(confusion_data_list): + clean_list = [] + for conf_item in confusion_data_list: + clean_list.append( + { + "gt label" : gen_latex_class_label( + class_label=conf_item['gt_label'] + ), + "pred label" : gen_latex_class_label( + class_label=conf_item['pred_label'] + ), + "score" : round(conf_item['score'], 1) + } + ) + return clean_list + + +def shrink_n_global_confusions( + test_results_path = "my_projects/test_results", + new_size=5, + new_name = None +): + for dataset_dir in os.listdir(test_results_path): + dataset_dir_path = os.path.join(test_results_path, dataset_dir) + json_file_path = os.path.join( + dataset_dir_path, + "data", + f"{dataset_dir}_confusion.json" + ) + data_dict = load_json_file( + json_file_path=json_file_path + ) + for model_name, confusion_dict_list in data_dict.items(): + confusion_dict_list = sorted( + confusion_dict_list, + key=lambda d : d["score"], + reverse=True + ) + confusion_dict_list = confusion_dict_list[:new_size] + data_dict[model_name] = confusion_dict_list + save_dict_as_json( + data_dict=data_dict, + dump_file_path=json_file_path.replace( + "_confusion.json", + "_confusion_5.json" + ) + ) + + +def collect_all_confusion_matrix_data( + test_results_path = "my_projects/test_results", + n_decimals = 1 +): + + for dataset_dir in os.listdir(test_results_path): + confusion_dict = {} + dataset_dir_path = os.path.join(test_results_path, dataset_dir) + for model_dir in os.listdir(dataset_dir_path): + if model_dir == "data": + continue + json_file_path = os.path.join( + dataset_dir_path, + model_dir, + "confusion_matrix/top_10_confusion.json" + ) + confusion_data_list = load_json_file( + json_file_path=json_file_path + ) + model_name = model_dir.split("_")[0] + confusion_dict[model_name] = confusion_data_list + confusion_data_save_path = os.path.join( + dataset_dir_path, + "data", + f"{dataset_dir}_confusion.json" + ) + save_dict_as_json( + data_dict=confusion_dict, + dump_file_path=confusion_data_save_path + ) + + +def clean_keys_scene( + json_file_path, + map_dict = map_basic2plot, + n_decimals = 2 +): + data = load_json_file(json_file_path=json_file_path) + clean_dict = {} + for model_name, model_data in data.items(): + clean_dict[model_name] = {} + for scene_name, scene_data in model_data.items(): + clean_dict[model_name][scene_name] = {} + for metric_name, metric_val in scene_data.items(): + new_val = round(metric_val, n_decimals) + if metric_name in map_dict.keys(): + new_key = map_dict[metric_name] + clean_dict[model_name][scene_name][new_key] = new_val + elif metric_name in map_dict.values(): + clean_dict[model_name][scene_name][metric_name] = new_val + save_dict_as_json( + data_dict = clean_dict, + dump_file_path = json_file_path.replace(".json", "_clean.json") + + ) + +def fix_model_names(data_dict: dict) -> dict: + new_data = {} + for model_name, model_data in data_dict.items(): + model_name_ = map_model_name(model_name=model_name) + new_data[model_name_] = model_data + return new_data + +def fix_metrics_keys_and_vals( + metric_dict: dict, + n_decimals = 1 +) -> dict: + new_dict = {} + for metric_name, metric_val in metric_dict.items(): + metric_name_ = map_metric_key_strict(key=metric_name) + if not metric_name_ or np.isnan(metric_val): + continue + new_dict[metric_name_] = round(metric_val, n_decimals) + return new_dict + +def fix_confusion_dict(confusion_dict: dict, n_decimals = 1) -> dict: + new_conf_dict = fix_model_names(data_dict=confusion_dict) + for model_name, conf_list in new_conf_dict.items(): + for conf_item in conf_list: + conf_item["score"] = round(conf_item["score"], n_decimals) + return new_conf_dict + +def fix_results_dict(results_dict: dict, n_decimals = 1) -> dict: + new_dict = fix_model_names(data_dict=results_dict) + for model_name, metric_dict in new_dict.items(): + new_dict[model_name] = fix_metrics_keys_and_vals( + metric_dict=metric_dict, + n_decimals=n_decimals + ) + return new_dict + +def fix_per_label_results_dict(results_dict: dict, n_decimals = 1) -> dict: + new_dict = fix_model_names(data_dict=results_dict) + for model_name, label_dict in new_dict.items(): + for label, metric_dict in label_dict.items(): + new_dict[model_name][label] = fix_metrics_keys_and_vals( + metric_dict=metric_dict, + n_decimals=n_decimals + ) + return new_dict + +def gen_default_save_path(source_json_path, save_app): + return source_json_path.replace( + ".json", + f"{save_app}.json" + ) + +def fix_jsons( + confusion_json_path = None, + global_results_json_path = None, + per_label_results_json_path = None, + confusion_json_save_path = None, + global_results_json_save_path = None, + per_label_results_json_save_path = None, + n_decimals = 1, + save_app = "" +): + if confusion_json_path is not None: + confusion_matrix = load_json_file( + json_file_path=confusion_json_path + ) + confusion_matrix = fix_confusion_dict( + confusion_dict=confusion_matrix, + n_decimals=n_decimals + ) + if confusion_json_save_path is None: + confusion_json_save_path = gen_default_save_path( + source_json_path=confusion_json_path, + save_app=save_app + ) + save_dict_as_json( + data_dict=confusion_matrix, + dump_file_path=confusion_json_save_path + ) + + if global_results_json_path is not None: + results_dict = load_json_file( + json_file_path=global_results_json_path + ) + results_dict = fix_results_dict( + results_dict=results_dict, + n_decimals=n_decimals + ) + if global_results_json_save_path is None: + global_results_json_save_path = gen_default_save_path( + source_json_path=global_results_json_path, + save_app=save_app + ) + save_dict_as_json( + data_dict=results_dict, + dump_file_path=global_results_json_save_path + ) + + if per_label_results_json_path is not None: + results_dict = load_json_file( + json_file_path=per_label_results_json_path + ) + results_dict = fix_per_label_results_dict( + results_dict=results_dict, + n_decimals=n_decimals + ) + if per_label_results_json_save_path is None: + per_label_results_json_save_path = gen_default_save_path( + source_json_path=per_label_results_json_path, + save_app=save_app + ) + save_dict_as_json( + data_dict=results_dict, + dump_file_path=per_label_results_json_save_path + ) + + + + +def main(): + # clean_keys_scene( + # json_file_path="my_projects/ablation_tests/test_results/data/scene_results.json" + # ) + test_results_path = "my_projects/conversion_tests/test_results" + for results_dir in os.listdir(test_results_path): + results_dir_path = os.path.join( + test_results_path, + results_dir + ) + confusion_json_path = os.path.join( + results_dir_path, + "data", + f"{results_dir}_confusion_top_5.json" + ) + global_results_json_path = os.path.join( + results_dir_path, + "data", + f"{results_dir}_results.json" + ) + per_label_results_json_path = os.path.join( + results_dir_path, + "data", + f"{results_dir}_per_label_results.json" + ) + fix_jsons( + confusion_json_path=confusion_json_path, + global_results_json_path=global_results_json_path, + per_label_results_json_path=per_label_results_json_path + ) + + +if __name__ == '__main__': + fix_jsons( + global_results_json_path="my_projects/test_results/sodhots-c/data/sodhots-c_results.json", + # confusion_json_path="my_projects/test_results/sodhots-c/data/sodhots-c_confusion_top_5.json" + # per_label_results_json_path="my_projects/test_results/sodhots-c/data/sodhots-c_per_label_results.json" + ) + # main() diff --git a/my_projects/scripts/generate_json_all_test_results.py b/my_projects/scripts/generate_json_all_test_results.py new file mode 100644 index 0000000000..d565ae8a03 --- /dev/null +++ b/my_projects/scripts/generate_json_all_test_results.py @@ -0,0 +1,357 @@ +import json +import os +import argparse +from my_projects.scripts.parse_test_log_table import parse_table + + +KEYS_OF_INTEREST = { + "performance" : [ + "mPr@50", + "mPr@60", + "mPr@70", + "mPr@80", + "mPr@90", + "mIoU" + ], + "benchmark" : [ + "Mem (MB)", + "Var Mem", + "FPS", + "Var FPS" + ] +} + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + '--dataset_path', + '-dsp', + type=str, + default=None, + help='directory path of dataset results or work dir' + ) + parser.add_argument( + '--save_intermediate', + '-si', + action='store_true' + ) + parser.add_argument( + '--label_results', + '-lr', + action='store_true' + ) + parser.add_argument( + '--confusion_results', + '-cr', + action='store_true' + ) + parser.add_argument( + '--general_results', + '-gr', + action='store_true' + ) + parser.add_argument( + '--n_confusion', + '-nc', + type=int, + default=5 + ) + + + args = parser.parse_args() + # if no specific result is marked, do all by default + if ( + not args.label_results + and + not args.confusion_results + and + not args.general_results + ): + args.label_results = True + args.confusion_results = True + args.general_results = True + return args + +def save_dict_as_json(data_dict: dict, dump_file_path: str): + print(f"saving to : {dump_file_path}") + with open(dump_file_path, 'w') as dump_file: + json.dump(data_dict, dump_file, indent=4) + +def load_json_file(json_file_path: str) -> dict: + with open(json_file_path, 'r') as file: + return json.load(file) + +def is_acc_test(dir_name: str) -> bool: + file_parts = dir_name.split("_") + if len(file_parts) < 2: + return False + return file_parts[0].isnumeric() and file_parts[1].isnumeric() + +def extract_json_file_name_from_dir(dir_path: str) -> str: + for file_name in os.listdir(dir_path): + if ".json" in file_name: + return file_name + return "" + +def get_data_dict(data_dict: dict, keys_of_interest: list) -> dict: + if keys_of_interest is None: + return data_dict + + return { + key : val for key, val in data_dict.items() + if key in keys_of_interest + } + +def get_data_dict_from_dir(dir_path: str, keys_of_interest: list) -> dict: + json_file_name = extract_json_file_name_from_dir( + dir_path=dir_path + ) + data_dict = load_json_file( + json_file_path=os.path.join( + dir_path, + json_file_name + ) + ) + return get_data_dict(data_dict=data_dict, keys_of_interest=keys_of_interest) + + + +def merg_dicts(dict_list: list) -> dict: + new_dict = {} + for dict_ in dict_list: + for key, val in dict_.items(): + new_dict[key] = val + return new_dict + +def get_top_n_confusion_values( + confusion_dict_list: list, + n_confusion_values: int +): + return sorted( + confusion_dict_list, + key=lambda d : d["score"], + reverse=True + )[:n_confusion_values] + +def generate_confusion_dict_dataset( + dataset_path: str, + n_confusion_values: int, + ignore_background: bool = True, +): + confusion_dict = {} + for model_dir in os.listdir(dataset_path): + if model_dir == "data": + continue + model_name = model_dir.split("_")[0] + model_results_path = os.path.join( + dataset_path, + model_dir + ) + if os.path.isfile(model_results_path): + continue + confusion_matrix_path = os.path.join( + model_results_path, + "confusion_matrix", + "confusion.json" + ) + if not os.path.exists(confusion_matrix_path): + print(f"no confusion matrix found at {confusion_matrix_path}") + continue + # load dict and get top n + confusion_matrix = load_json_file(json_file_path=confusion_matrix_path) + if ignore_background: + + confusion_matrix = [ + data_dict for data_dict in confusion_matrix + if "_background_" not in data_dict.values() + ] + # append top n dict to conf_dict + confusion_dict[model_name] = get_top_n_confusion_values( + confusion_dict_list=confusion_matrix, + n_confusion_values=n_confusion_values + ) + return confusion_dict + +def generate_dict_model_results(model_results_path: str) -> dict: + model_data_dict = {} + for dir_name in os.listdir(model_results_path): + if dir_name == "data": + continue + dir_path = os.path.join(model_results_path, dir_name) + if os.path.isfile(dir_path): + continue + dict_ = {} + if is_acc_test(dir_name=dir_name): + dict_ = get_data_dict_from_dir( + dir_path=dir_path, + keys_of_interest=KEYS_OF_INTEREST['performance'] + ) + elif dir_name == "benchmark": + dict_ = get_data_dict_from_dir( + dir_path=dir_path, + keys_of_interest=KEYS_OF_INTEREST['benchmark'] + ) + else: + continue + model_data_dict = merg_dicts( + dict_list=[ + model_data_dict, + dict_ + ] + ) + return model_data_dict + +def generate_dict_dataset_results( + dataset_path: str, + save_intermediate: bool = False +) -> dict: + dataset_results_dict = {} + for model_dir in os.listdir(dataset_path): + if model_dir == "data": + continue + model_name = model_dir.split("_")[0] + model_results_path = os.path.join( + dataset_path, + model_dir + ) + if os.path.isfile(model_results_path): + continue + dataset_results_dict[model_name] = generate_dict_model_results( + model_results_path=model_results_path + ) + if save_intermediate: + save_dict_as_json( + data_dict=dataset_results_dict[model_name], + dump_file_path=os.path.join( + model_results_path, + f"{model_name}_results.json" + ) + ) + return dataset_results_dict + + +def generate_dict_per_label_model( + model_results_path: str +) -> dict: + + for dir_name in os.listdir(model_results_path): + if dir_name == "data": + continue + dir_path = os.path.join(model_results_path, dir_name) + if os.path.isfile(dir_path): + continue + dict_ = {} + if is_acc_test(dir_name=dir_name): + for file in os.listdir(dir_path): + if ".log" in file: + log_path = os.path.join( + dir_path, + file + ) + print(f"log path: {log_path}\n") + table_dict = parse_table( + log_path=log_path + ) + return table_dict + return None + + +def get_per_label_results_dataset( + dataset_path: str, + save_intermediate: bool = False +) -> dict: + dataset_results_dict = {} + for model_dir in os.listdir(dataset_path): + if model_dir == "data": + continue + model_name = model_dir.split("_")[0] + model_results_path = os.path.join( + dataset_path, + model_dir + ) + if os.path.isfile(model_results_path): + continue + dataset_results_dict[model_name] = generate_dict_per_label_model( + model_results_path=model_results_path + ) + if save_intermediate: + save_dict_as_json( + data_dict=dataset_results_dict[model_name], + dump_file_path=os.path.join( + model_results_path, + f"{model_name}_label_results.json" + ) + ) + return dataset_results_dict + +def get_model_results_path(model_name: str, dataset_path: str): + for model_res_name in os.listdir(dataset_path): + if model_name in model_res_name: + return os.path.join( + dataset_path, + model_res_name + ) + + + +def main(): + args = parse_args() + if not args.dataset_path: + return + + data_dir_path = os.path.join( + args.dataset_path, + "data" + ) + if not os.path.exists(data_dir_path): + os.mkdir(data_dir_path) + _, dataset_name = os.path.split(args.dataset_path) + if args.dataset_path[-1] == '/' or dataset_name == '': + _, dataset_name = os.path.split(args.dataset_path[:-1]) + if args.general_results: + dataset_results_dict = generate_dict_dataset_results( + dataset_path=args.dataset_path, + save_intermediate=args.save_intermediate + ) + + save_dict_as_json( + data_dict=dataset_results_dict, + dump_file_path=os.path.join( + data_dir_path, + f"{dataset_name}_results.json" + ) + ) + if args.confusion_results: + dataset_confusion_dict = generate_confusion_dict_dataset( + dataset_path=args.dataset_path, + n_confusion_values=args.n_confusion + ) + + save_dict_as_json( + data_dict=dataset_confusion_dict, + dump_file_path=os.path.join( + data_dir_path, + f"{dataset_name}_confusion_top_{args.n_confusion}.json" + ) + ) + if args.label_results: + label_results = get_per_label_results_dataset( + dataset_path=args.dataset_path, + save_intermediate=args.save_intermediate + ) + + save_dict_as_json( + data_dict=label_results, + dump_file_path=os.path.join( + data_dir_path, + f"{dataset_name}_per_label_results.json" + ) + ) + + + + + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/my_projects/scripts/generate_latex_tables.py b/my_projects/scripts/generate_latex_tables.py new file mode 100644 index 0000000000..6b1a65a08c --- /dev/null +++ b/my_projects/scripts/generate_latex_tables.py @@ -0,0 +1,189 @@ +from copy import deepcopy +import json + +# TODO change std out to filepath + +LABEL_TABLE_TEMPLATE = """ + \\begin{table}[ht] + \centering + \caption{CAPTION} + \\begin{adjustbox}{width=\columnwidth,center} + \\begin{tabular}{@{}l|rrrrrr@{}} + \\toprule + # + \\bottomrule + \end{tabular} + \end{adjustbox} + \label{LABEL} + \end{table} + """ +LABEL_MODEL_TABLE_TEMPLATE = """ + \\begin{table}[ht] + \centering + \caption{CAPTION} + \\begin{tabular}{@{}l|ccccc@{}} + \\toprule + Model & MODEL1 & MODEL2 & MODEL3 & MODEL4 & MODEL5 \\\\ + \\midrule + Label & \\multicolumn{5}{c}{IoU} \\\\ + \\midrule + # + \\bottomrule + \end{tabular} + \label{LABEL} + \end{table} + """ +def get_label_tab_template(model_name, dataset_name): + template = deepcopy(LABEL_TABLE_TEMPLATE) + caption=f"Performance Metrics per Class {model_name} ({dataset_name})" + label=f"tab:per_class_{model_name}_{dataset_name.lower().replace(' ', '_')}" + template = template.replace("CAPTION", caption) + template = template.replace("LABEL", label) + return template + +def get_label_model_template(dataset_name, model_names): + template = deepcopy(LABEL_MODEL_TABLE_TEMPLATE) + caption=f"Performance Metrics per Class {dataset_name}" + label=f"tab:per_class_{dataset_name.lower().replace(' ', '_')}" + template = template.replace("CAPTION", caption) + template = template.replace("LABEL", label) + for model_idx, model_name in enumerate(model_names): + template = template.replace(f"MODEL{model_idx + 1}", model_name) + return template + +def per_label_table( + label_results_dict, + model_name, + dataset_name, + col_names = [ + "Class", "Pr\\textsubscript{50}", + "Pr\\textsubscript{60}", + "Pr\\textsubscript{70}", + "Pr\\textsubscript{80}", + "Pr\\textsubscript{90}", + "IoU" + ] + +): + template = get_label_tab_template( + model_name=model_name, dataset_name=dataset_name + ) + print(template.split("#")[0]) + print_str = "" + for col_idx, col_name in enumerate(col_names): + if col_idx == len(col_names) - 1: + print_str = f"{print_str} {col_name} \\\\" + else: + print_str = f"{print_str} {col_name} &" + print(f"\t\t{print_str}") + print("\t\t\midrule") + + for class_name, class_data in label_results_dict.items(): + class_name_ = class_name.replace('_', '\_') + print_str = f"\\texttt{{{class_name_}}} \t&" + for metric_idx, (metric_name, metric_val) in enumerate(class_data.items()): + if metric_idx == len(class_data.items()) - 1: + print_str = f"{print_str} {metric_val} \t \\\\" + else: + print_str = f"{print_str} {metric_val} \t &" + print(f"\t\t{print_str}") + print(template.split("#")[-1]) + + +def get_per_label_tables_all_models(data_dict, dataset_name): + for model_name, label_results_dict in data_dict.items(): + per_label_table( + label_results_dict=label_results_dict, + model_name=model_name, + dataset_name=dataset_name + ) + print('\n' * 2) + +def save_dict_as_json(data_dict: dict, dump_file_path: str): + print(f"saving to : {dump_file_path}") + with open(dump_file_path, 'w') as dump_file: + json.dump(data_dict, dump_file, indent=4) + +def load_json_file(json_file_path: str) -> dict: + with open(json_file_path, 'r') as file: + return json.load(file) + +def group_data_by_class_label(label_results_dict): + grouped_dict = {} + for model_name, label_data in label_results_dict.items(): + for label_name, label_metrics in label_data.items(): + if label_name not in grouped_dict.keys(): + grouped_dict[label_name] = {} + grouped_dict[label_name][model_name] = label_metrics["IoU"] + return grouped_dict + +def per_label_per_model_std( + label_results_dict, + dataset_name, + template = LABEL_MODEL_TABLE_TEMPLATE +): + + model_names = list(label_results_dict.keys()) + template = get_label_model_template( + dataset_name=dataset_name, + model_names=model_names + ) + print(template.split("#")[0]) + label_results_dict = group_data_by_class_label( + label_results_dict=label_results_dict + ) + for class_name, class_data in label_results_dict.items(): + class_name_ = class_name.replace('_', '\_') + print_str = f"\\texttt{{{class_name_}}} \t&" + for model_idx, (model_name, iou_val) in enumerate(class_data.items()): + if model_idx == len(class_data.items()) - 1: + print_str = f"{print_str} {iou_val} \t \\\\" + else: + print_str = f"{print_str} {iou_val} \t &" + print(f"\t\t{print_str}") + print(template.split("#")[-1]) + +def per_label_per_model( + label_results_dict, + dataset_name, + template = LABEL_MODEL_TABLE_TEMPLATE, + file_path = None +): + if file_path is None: + return per_label_per_model_std( + label_results_dict=label_results_dict, + dataset_name=dataset_name, + template=template, + ) + with open(file_path, 'w') as table_file: + model_names = list(label_results_dict.keys()) + template = get_label_model_template( + dataset_name=dataset_name, + model_names=model_names + ) + table_file.write(f"{template.split('#')[0]}\n") + label_results_dict = group_data_by_class_label( + label_results_dict=label_results_dict + ) + for class_name, class_data in label_results_dict.items(): + class_name_ = class_name.replace('_', '\_') + print_str = f"\\texttt{{{class_name_}}} \t&" + for model_idx, (model_name, iou_val) in enumerate(class_data.items()): + if model_idx == len(class_data.items()) - 1: + print_str = f"{print_str} {iou_val} \t \\\\" + else: + print_str = f"{print_str} {iou_val} \t &" + table_file.write(f"\t\t{print_str}\n") + table_file.write(f"{template.split('#')[-1]}\n") + +def main(): + file_path = "my_projects/test_results/irl_vision_cat/data/irl_vision_cat_per_label_results.json" + data_dict = load_json_file(json_file_path=file_path) + per_label_per_model( + label_results_dict=data_dict, + dataset_name="IRL Vision Cat" + ) + + +if __name__ == '__main__': + main() diff --git a/my_projects/scripts/gt_img_pair.bat b/my_projects/scripts/gt_img_pair.bat new file mode 100644 index 0000000000..05e56f3246 --- /dev/null +++ b/my_projects/scripts/gt_img_pair.bat @@ -0,0 +1,7 @@ +python my_projects/scripts/visualize_preds.py my_projects/test_results/hots_v1/ hots -raw -gt -sn HOTS_set -sp /home/ids/Desktop +python my_projects/scripts/visualize_preds.py my_projects/test_results/hots_v1_cat/ hots-c -raw -gt -sn HOTS-C_set -sp /home/ids/Desktop +python my_projects/scripts/visualize_preds.py my_projects/test_results/irl_vision/ irl_vision -raw -gt -sn SOD_set -sp /home/ids/Desktop +python my_projects/scripts/visualize_preds.py my_projects/test_results/irl_vision_cat/ irl_vision_cat -raw -gt -sn SOD-C_set -sp /home/ids/Desktop +python my_projects/scripts/visualize_preds.py my_projects/test_results/arid20_cat/ ARID20 -raw -gt -sn ARID20_set -sp /home/ids/Desktop +python my_projects/scripts/visualize_preds.py my_projects/test_results/arid10_cat/ ARID10 -raw -gt -sn ARID10_set -sp /home/ids/Desktop + diff --git a/my_projects/scripts/organise_clutter_results.py b/my_projects/scripts/organise_clutter_results.py new file mode 100644 index 0000000000..e65d2dec7d --- /dev/null +++ b/my_projects/scripts/organise_clutter_results.py @@ -0,0 +1,40 @@ +import json +import os +import argparse + + +def find_json_file(n_objects_dir_path): + for root, dirs, files in os.walk(os.path.join(n_objects_dir_path)): + json_file = [file for file in files if ".json" in file] + if json_file: + return os.path.join(root, json_file[0]) + return None + +def collect_json_files(clutter_path): + clutter_dict = {} + for n_objects_dir in os.listdir(clutter_path): + json_file_path = find_json_file( + n_objects_dir_path=os.path.join( + clutter_path, + n_objects_dir + ) + ) + if json_file_path is None: + continue + with open(json_file_path, 'r') as json_file: + clutter_dict[int(n_objects_dir)] = json.load(json_file) + clutter_dump_path = os.path.join(clutter_path, "clutter_data.json") + with open(clutter_dump_path, 'w') as dump_file: + json.dump(clutter_dict, dump_file, indent=4) + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + 'clutter_path', + type=str + ) + args = parser.parse_args() + collect_json_files(args.clutter_path) + +if __name__ == '__main__': + main() diff --git a/my_projects/scripts/parse_model_data.py b/my_projects/scripts/parse_model_data.py new file mode 100644 index 0000000000..6eefb25b6f --- /dev/null +++ b/my_projects/scripts/parse_model_data.py @@ -0,0 +1,310 @@ +import os +from mmengine import Config +from copy import copy, deepcopy +import markdown +import re +from mmengine.analysis.complexity_analysis import ( + parameter_count_table, + parameter_count +) +from mmengine.registry import init_default_scope + +from mmseg.registry import MODELS + +def parse_row(row_str): + row_ = row_str.split("|") + row = [] + for item in row_: + if item == "" or item == "\n": + continue + row.append(item.strip()) + return row + +def is_table_row(line): + if len(line) < 2: + return False + return line[0] == "|" and line[-1] == "|" + +def is_first_row(row): + return row[0] == "Method" and row[1] == "Backbone" + +def is_sep_row(row): + return "--" in row[0] + +def extract_dataset_name(line): + if len(line) < 4: + return "" + if not line[:3] == "###": + return "" + trimmed_line = line[4:] + trimmed_line = trimmed_line[:-1] + return trimmed_line + +def same_model_diff_sched(model_dict0, model_dict1): + if model_dict0 is model_dict1: + return False + if model_dict0["Method"] != model_dict1["Method"]: + return False + if model_dict0["Backbone"] != model_dict1["Backbone"]: + return False + + if model_dict0["Crop Size"] != model_dict1["Crop Size"]: + return False + + if model_dict0["dataset"] != model_dict1["dataset"]: + if ( + ( + model_dict0["dataset"] == "Pascal Context" + and + model_dict1["dataset"] == "Pascal Context 59" + ) + or + ( + model_dict1["dataset"] == "Pascal Context" + and + model_dict0["dataset"] == "Pascal Context 59" + ) + ): + return True + return False + return True + +def complete_model_dict(model_dict, model_dict_list): + for model_dict_ in model_dict_list: + if model_dict is model_dict_: + continue + if same_model_diff_sched( + model_dict0=model_dict_, + model_dict1=model_dict + ): + if model_dict["Mem (GB)"] == '-' and model_dict_["Mem (GB)"] != '-': + model_dict["Mem (GB)"] = model_dict_["Mem (GB)"] + if ( + model_dict["Inf time (fps)"] == '-' + and + model_dict_["Inf time (fps)"] != '-' + ): + model_dict["Inf time (fps)"] = model_dict_["Inf time (fps)"] + return model_dict + return None # TODO not sure just for test + + +def fill_in_missing_vals( + model_dict_list, + keys_to_fix = [ + "Mem (GB)", + "Inf time (fps)", + "mIoU" + ] +): + model_dict_list_ = [] + for model_dict in model_dict_list: + if '-' in [ + value for key, value in model_dict.items() if key in keys_to_fix + ]: + # if '-' in model_dict.values(): + model_dict = complete_model_dict( + model_dict=model_dict, + model_dict_list=model_dict_list + ) + if model_dict is None: + continue + model_dict_list_.append(model_dict) + return model_dict_list_ + +def fix_dict_types( + model_dict_list, + keys_to_fix = [ + "Mem (GB)", + "Inf time (fps)", + "mIoU" + ] +): + model_dict_list_ = [] + for model_dict in model_dict_list: + for key in keys_to_fix: + try: + val = re.findall(r"[-+]?(?:\d*\.*\d+)", model_dict[key])[0] + + except: + continue + model_dict[key] = float(val) + model_dict_list_.append(model_dict) + return model_dict_list_ + +def dict_keys_valid( + model_dict, + essential_keys = [ + "Method", + "Backbone", + "Crop Size", + "dataset", + "Mem (GB)", + "Inf time (fps)", + "mIoU" + ] +): + for key in essential_keys: + if key not in model_dict.keys(): + + + return False + return True + +def comparing_dict_vals_valid( + model_dict, + comparing_keys = [ + "Mem (GB)", + "Inf time (fps)", + "mIoU" + ] +): + for key in comparing_keys: + if not isinstance(model_dict[key], (int, float)): + return False + return True +def get_comparable_model_dict_list( + model_dict_list, + comparing_keys = [ + "Mem (GB)", + "Inf time (fps)", + "mIoU" + ] +): + model_dict_list_ = [] + for model_dict in model_dict_list: + if not comparing_dict_vals_valid( + model_dict=model_dict, + comparing_keys=comparing_keys + ): + continue + model_dict_list_.append(model_dict) + + return model_dict_list_ + +def remove_invalid_dicts( + model_dict_list, + essential_keys = [ + "Method", + "Backbone", + "Crop Size", + "dataset", + "Mem (GB)", + "Inf time (fps)", + "mIoU" + ] +): + model_dict_list_ = [] + for model_dict in model_dict_list: + + if model_dict is None: + continue + if not dict_keys_valid( + model_dict=model_dict, essential_keys=essential_keys + ): + continue + + if not os.path.exists(model_dict["config"]): + continue + model_dict_list_.append(model_dict) + return model_dict_list_ + + + +def is_dataset_name( + line, + +): + if len(line) < 4: + return False + if not line[:3] == "###": + return False + dataset_name = extract_dataset_name(line=line) + if dataset_name == "": + return False + return True + +def make_model_dict(row, data_set_name, labels): + model_dict = {} + for label in labels: + if label == "download": + continue + if label == "config": + config_path = row[labels.index(label)].split("main/")[-1][:-1] + model_dict["config"] = config_path + continue + model_dict[label] = row[labels.index(label)] + model_dict["dataset"] = data_set_name + return model_dict + +def get_readme_data( + project_path, +): + readme_path = os.path.join(project_path, "README.md") + if not os.path.exists(readme_path): + return None + model_dict_list = [] + current_dataset_name = "" + labels = [] + + with open(readme_path, 'r') as readme_file: + for line in readme_file.readlines(): + if is_dataset_name(line=line): + current_dataset_name = extract_dataset_name(line=line) + + + + if is_table_row(line=line[:-1]): + row = parse_row(row_str=line) + if is_sep_row(row=row): + continue + + if is_first_row(row=row): + labels = row + else: + model_dict_list.append(make_model_dict( + row=row, + data_set_name=current_dataset_name, + labels=labels + ) + ) + return model_dict_list + +def postprocesses_model_dict_list(model_dict_list): + model_dict_list = remove_invalid_dicts(model_dict_list=model_dict_list) + if model_dict_list == []: + return [] + # fill in missing values + model_dict_list = fill_in_missing_vals(model_dict_list=model_dict_list) + + # fix types + model_dict_list = fix_dict_types(model_dict_list=model_dict_list) + + # filter out last faulty data + model_dict_list = get_comparable_model_dict_list( + model_dict_list=model_dict_list + ) + + return model_dict_list + +def get_all_model_dicts( + project_list, + config_root_path = "configs/", +): + + model_dict_list = [] + for project_name in project_list: + project_path = os.path.join(config_root_path, project_name) + project_model_dict_list = get_readme_data( + project_path=project_path + ) + + if project_model_dict_list is None: + continue + + project_model_dict_list = postprocesses_model_dict_list( + model_dict_list=project_model_dict_list + ) + model_dict_list += project_model_dict_list + return model_dict_list + diff --git a/my_projects/scripts/parse_test_log_table.py b/my_projects/scripts/parse_test_log_table.py new file mode 100644 index 0000000000..32bc08ed35 --- /dev/null +++ b/my_projects/scripts/parse_test_log_table.py @@ -0,0 +1,82 @@ +import argparse +import json + +def save_json(data_dict: dict, dump_file_path: str): + print(f"saving to : {dump_file_path}") + with open(dump_file_path, 'w') as dump_file: + json.dump(data_dict, dump_file, indent=4) + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + 'log_path', + type=str + ) + parser.add_argument( + 'save_path', + type=str + ) + + args = parser.parse_args() + + return args + +def extract_items_from_line(line): + items = line.split("|") + return [ + item.strip() for item in items + if len(item.strip()) != 0 + ] + +def parse_table(log_path: str) -> dict: + with open(log_path, 'r') as log_file: + line = log_file.readline() + # skip until table + while line: + # if "Class" in line and "IoU": + # break + if "+---" in line: + line = log_file.readline() + break + line = log_file.readline() + if not line: + print(f"table not found in {log_path}") + return + # start parsing + + keys = extract_items_from_line(line=line) + # split(".") is temp due to false keys in some logs + keys = [ + key.split(".")[0] for key in keys + ][1:] + + print(keys) + # line to skip + line = log_file.readline() + # start parsing vals + line = log_file.readline() + table_dict = {} + while "+---" not in line: + vals = extract_items_from_line(line=line) + class_name = vals[0] + vals = [ + float(val) for val in vals[1:] + ] + table_dict[class_name] = { + key : val for key, val in zip(keys, vals) + } + line = log_file.readline() + return table_dict + + + +def main(): + args = parse_args() + table_dict = parse_table(log_path=args.log_path) + save_json( + data_dict=table_dict, + dump_file_path=args.save_path + ) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/my_projects/scripts/plot_clutter_data.py b/my_projects/scripts/plot_clutter_data.py new file mode 100644 index 0000000000..1e5c13573f --- /dev/null +++ b/my_projects/scripts/plot_clutter_data.py @@ -0,0 +1,181 @@ +from matplotlib import pyplot as plt +import numpy as np +import json +import os +import argparse +import my_projects.scripts.plotting_utils as p_utils + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + '--clutter_path', + type=str, + default="my_projects/test_results/arid20_cat/", + help='directory path of clutter experiment' + ) + parser.add_argument( + "--save_dir_path", + type=str, + default="my_projects/images_plots/clutter" + ) + + parser.add_argument( + '--global_plot', + '-gp', + action='store_true' + ) + parser.add_argument( + '--per_model', + '-pm', + action='store_true' + ) + + args = parser.parse_args() + + return args + + +def get_data_dict(json_path): + data = None + with open(json_path, 'r') as json_file: + data = json.load(json_file) + return data + +def reorganize_clutter_dict(clutter_data_dict): + metrics_names = [ + "mPr@50", "mPr@60", "mPr@70", + "mPr@80", "mPr@90", "mIoU" + ] + cl_dict_new = { + metric_name : np.zeros(len(clutter_data_dict)) + for metric_name in metrics_names + } + for n_objects, data_dict in clutter_data_dict.items(): + for metric_name, metric_val in data_dict.items(): + # TODO temp split due to wrong notation old tests + metric_name = p_utils.map_metric_key(key=metric_name) + if metric_name not in metrics_names: + continue + cl_dict_new[metric_name][int(n_objects) - 1] = metric_val + return cl_dict_new + +def make_plot( + clutter_data_dict, + save_path, + xlabel="n objects", + ylabel="score (%)" +): + x_axis = [ + int(n_objects + 1) for n_objects in range( + len( + clutter_data_dict[list(clutter_data_dict.keys())[0]] + ) + ) + ] + y_axis = np.arange(0, 110, 10) + legend_handles = [] + # important moments 1-9 separated, 10-16 phys touch, 17-20 stacked + for data_point in [9, 16, 20]: + plt.axvline(data_point, color='k', ls ='dotted', linewidth=2.5) + for metric_name, data in clutter_data_dict.items(): + metric_name = p_utils.map_metric_key_plot(metric_name) + handle, = plt.plot( + x_axis, + data, + label=metric_name + ) + legend_handles.append( + handle + ) + # legend_labels.append(p_utils.fix_metric_name(metric_name)) + if ".png" not in save_path: + save_path = f"{save_path}.png" + plt.legend( + handles=legend_handles, + bbox_to_anchor=(0, 0.98, 1, 0.25), + ncol=3, + mode="expand" + ) + plt.xticks(x_axis) + plt.yticks(y_axis) + plt.ylim(48, 101) + plt.ylabel(ylabel) + plt.xlabel(xlabel) + plt.tight_layout() + plt.savefig(fname=save_path, format='png') + plt.clf() + +def get_clutter_dict(clutter_path: str): + for file_name in os.listdir(clutter_path): + if ".json" in file_name: + clutter_dict = get_data_dict( + json_path=os.path.join( + clutter_path, + file_name + ) + ) + clutter_dict = reorganize_clutter_dict( + clutter_data_dict=clutter_dict + ) + return clutter_dict + +def plot_clutter_per_model( + clutter_dict, save_path +): + + make_plot( + clutter_data_dict=clutter_dict, + save_path=save_path + ) + +def global_plot(args): + dataset_path = args.clutter_path + global_miou_dict = {} + for model_dir in os.listdir(dataset_path): + if model_dir == "data": + continue + model_path = os.path.join(dataset_path, model_dir) + if os.path.isfile(model_path): + continue + clutter_path = os.path.join( + model_path, + "clutter" + ) + clutter_dict = get_clutter_dict( + clutter_path=clutter_path + ) + + model_name = model_dir.split("_")[0] + if args.per_model: + plot_clutter_per_model( + clutter_dict=clutter_dict, + save_path=os.path.join( + args.save_dir_path, + f"{model_name}_clutter.png" + ) + ) + model_name_ = p_utils.map_model_name(model_name=model_name) + global_miou_dict[model_name_] = clutter_dict["mIoU"] + save_path = os.path.join(args.save_dir_path, f"global_clutter") + make_plot( + clutter_data_dict=global_miou_dict, + save_path=save_path, + xlabel="n objects", ylabel="mIoU" + ) + +def main(): + args = parse_args() + p_utils.set_params(param_dict=p_utils.clutter_plot) + if args.global_plot: + global_plot(args=args) + else: + clutter_dict = get_clutter_dict(clutter_path=args.clutter_path) + print(clutter_dict) + plot_clutter_per_model( + clutter_dict=clutter_dict, + save_path=args.save_dir_path + ) + p_utils.reset_params() + +if __name__ == '__main__': + main() diff --git a/my_projects/scripts/plot_legends.py b/my_projects/scripts/plot_legends.py new file mode 100644 index 0000000000..7904b8873b --- /dev/null +++ b/my_projects/scripts/plot_legends.py @@ -0,0 +1,123 @@ +import argparse +import os +from copy import copy +import numpy as np +from PIL import Image +from matplotlib import pyplot as plt +import matplotlib.lines as mlines +import plotting_utils as p_utils +from mmengine.structures import PixelData +from mmseg.utils import ( + hots_v1_classes, + hots_v1_palette, + irl_vision_sim_classes, + irl_vision_sim_palette, + hots_v1_cat_classes, + hots_v1_cat_palette, + irl_vision_sim_cat_classes, + irl_vision_sim_cat_palette, + arid20cat_classes, + arid20cat_palette, + arid10cat_classes, + arid10cat_palette, + sodhots_c_classes, + sodhots_c_palette, +) + +dataset_dict = { + "HOTS" : ( + hots_v1_classes(), + hots_v1_palette() + ), + "HOTS-C" : ( + hots_v1_cat_classes(), + hots_v1_cat_palette() + ), + "SOD" : ( + irl_vision_sim_classes(), + irl_vision_sim_palette() + ), + "SOD-C" : ( + irl_vision_sim_cat_classes(), + irl_vision_sim_cat_palette() + ), + "ARID10" : ( + arid10cat_classes(), + arid10cat_palette() + ), + "ARID20" : ( + arid20cat_classes(), + arid20cat_palette() + ), + "SODHOTS-C" : ( + sodhots_c_classes(), + sodhots_c_palette() + ) +} + + +def plot_legend( + name: str, + classes, + palette +): + + fig, ax = plt.subplots() + + legend_handles = generate_legend_handles(classes=classes, palette=palette) + + # Add the legend + ax.legend(handles = legend_handles, ncol=3, loc='center') + + + # Remove the axes ticks and labels (optional, for a cleaner look) + ax.set_xticks([]) + ax.set_yticks([]) + ax.set_xticklabels([]) + ax.set_yticklabels([]) + ax.spines['top'].set_visible(False) # Hide border lines + ax.spines['right'].set_visible(False) + ax.spines['bottom'].set_visible(False) + ax.spines['left'].set_visible(False) + + # Or, even simpler to remove everything related to the axes: + # ax.axis('off') + + # Adjust the figure size if needed to fit the legend nicely + fig.set_figheight(2) + fig.set_figwidth(2) + fig.tight_layout() # Ensures the legend fits within the figure bounds. Important! + + # Display the figure + # plt.show() + save_path = "my_projects/images_plots/legends" + fig.savefig( + os.path.join( + save_path, + f"{name}_legend.png" + ), + bbox_inches='tight' + ) + + +def generate_legend_handles( + classes, + palette +): + legend_handles = [] + for class_name, class_color in zip(classes, palette): + class_color = tuple([color/255 for color in class_color]) + legend_handles.append( + mlines.Line2D( + [], [], + color=class_color, + label=class_name.strip("_"), + linestyle='-', + linewidth=10 + ) + ) + return legend_handles + + +for name, (classes, palette) in dataset_dict.items(): + plot_legend(name, classes=classes, palette=palette) \ No newline at end of file diff --git a/my_projects/scripts/plot_per_label_results.py b/my_projects/scripts/plot_per_label_results.py new file mode 100644 index 0000000000..65d48078b3 --- /dev/null +++ b/my_projects/scripts/plot_per_label_results.py @@ -0,0 +1,63 @@ + +from matplotlib import pyplot as plt +from my_projects.scripts.generate_latex_tables import load_json_file +import os +import argparse + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + 'data_path', + type=str, + default="my_projects/test_results/arid20_cat/", + help='directory path of experiment' + ) + args = parser.parse_args() + if os.path.exists(os.path.join(args.data_path, "data")): + args.data_path = os.path.join(args.data_path, "data") + return args + +def save_class_hist_plot(class_hist, save_path): + + plt.rc('font', size=25) + plt.figure(figsize=(30, 6)) + plt.bar(class_hist.keys(), class_hist.values(), width=0.8) + plt.xticks( + range(len(class_hist.keys())), class_hist.keys(), + rotation = 90 + ) + plt.ylabel("IoU") + plt.ylim(48, 101) + + plt.savefig(save_path, bbox_inches='tight',dpi=100) + plt.clf() + +def make_class_hist(model_data_dict, metric = "IoU"): + return { + class_label : metric_dict[metric] + for class_label, metric_dict in model_data_dict.items() + } + + + +def main(): + args = parse_args() + data_path = args.data_path + file_name = [ + pth for pth in os.listdir(data_path) + if "per_label_results.json" in pth + ][0] + file_path = os.path.join(data_path, file_name) + data_dict = load_json_file(json_file_path=file_path) + for model_name, model_data in data_dict.items(): + hist = make_class_hist(model_data_dict=model_data) + save_class_hist_plot( + class_hist=hist, + save_path=os.path.join( + data_path, + f"{model_name}_per_label_hist.png" + ) + ) + +if __name__ == '__main__': + main() diff --git a/my_projects/scripts/plot_scene_results.py b/my_projects/scripts/plot_scene_results.py new file mode 100644 index 0000000000..8fca913358 --- /dev/null +++ b/my_projects/scripts/plot_scene_results.py @@ -0,0 +1,421 @@ +from matplotlib import pyplot as plt +import numpy as np +import json +import os +import argparse +import plotting_utils as p_utils + + + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + '--scene_results_json_path', + '-load', + type=str, + help='directory path of scene results', + default='my_projects/ablation_tests/test_results/data/scene_results.json' + ) + parser.add_argument( + '--save_dir_path', + '-dir', + type=str, + default="my_projects/images_plots/ablation" + ) + + parser.add_argument( + '--global_plot', + '-gp', + action='store_true' + ) + parser.add_argument( + '--per_model', + '-pm', + action='store_true' + ) + parser.add_argument( + '--single_model', + '-sm', + action='store_true' + ) + parser.add_argument( + '--show', + action='store_true' + ) + parser.add_argument( + '--dont_save', + action='store_true' + ) + # parser.add_argument( + # '--average_test_results_path', + # '-av', + # type=str, + # default=None + # ) + parser.add_argument( + '--include_av', + '-incl', + action='store_true' + ) + parser.add_argument( + '--plot_sep', + '-sep', + action='store_true' + ) + + + + args = parser.parse_args() + if args.single_model: + args.global_plot = False + args.per_model = False + # if args.include_av and args.average_test_results_path is None: + # args.average_test_results_path = "my_projects/test_results/arid20_cat/data/arid20_cat_performance_results.json" + return args + + +def get_model_dir_name(scene__data_path: str): + idx_ = -2 if scene__data_path[-1] == '/' else -1 + return scene__data_path.split("/")[idx_] + +def get_data_dict(json_path): + data = None + with open(json_path, 'r') as json_file: + data = json.load(json_file) + return data + +def global_data_grouped_by_scene( + data_dict, + metric = "mIoU", + include_av = False +): + grouped_dict = { + "floor" : [], + "table" : [], + "bottom" : [], + "top" : [] + } + if include_av: + grouped_dict['overall'] = [] + for model_name, model_data in data_dict.items(): + for scene_name, scene_data in model_data.items(): + if scene_name not in grouped_dict.keys(): + continue + grouped_dict[scene_name].append( + round(scene_data[metric], 1) + ) + + return grouped_dict + +def seperate_by_scene_type( + grouped_by_scene, + scene_type_sets = { + "viewpoint" : ["bottom", "top"], + "place" : ["floor", "table"] + } +): + seperated = {} + for scene_type, scene_type_names in scene_type_sets.items(): + seperated[scene_type] = { + scene_name : scene_data + for scene_name, scene_data in grouped_by_scene.items() + if scene_name in scene_type_names + } + return seperated + + + +def global_plot( + data_dict, + save_dir_path, + save_file_name = "global", + save = True, + show = False, + metric = "mIoU", + include_av = False, + plot_sep = False +): + + models = tuple( + p_utils.map_model_name(model_name=model_name) + for model_name in data_dict.keys() + ) + data_dict = fix_metric_keys_global_data_dict(data_dict=data_dict) + grouped_by_scene = global_data_grouped_by_scene( + data_dict=data_dict, + metric=metric, + include_av=include_av + ) + param_dict = p_utils.scenes_plot_global + seperated_by_scene_type = { + "all" : grouped_by_scene + } + + if plot_sep: + seperated_by_scene_type = seperate_by_scene_type( + grouped_by_scene=grouped_by_scene + ) + param_dict = p_utils.scenes_plot_global_per_scene_type + for scene_type, scene_data in seperated_by_scene_type.items(): + plot_global_plot( + grouped_by_scene=scene_data, + models=models, + save_dir_path=save_dir_path, + save_file_name=f"{save_file_name}_{scene_type}", + save=save, + show=show, + metric=metric, + param_dict=param_dict + ) + +def plot_global_plot( + grouped_by_scene, + models, + save_dir_path, + save_file_name = "global", + save = True, + show = False, + metric = "mIoU", + param_dict=p_utils.scenes_plot_global +): + p_utils.set_params(param_dict=param_dict) + x_axis = np.arange(len(models)) + bar_width = 0.96 / len(grouped_by_scene.keys()) + relative_positions = get_relative_positions( + grouped_dict=grouped_by_scene + ) + for scene_idx, scene_item in enumerate(grouped_by_scene.items()): + (scene_name, scene_value) = scene_item + offset = (bar_width) * relative_positions[scene_idx] + rects = plt.bar( + x_axis + offset, + scene_value, + bar_width, + label=scene_name + ) + plt.bar_label(rects, padding=3, fontsize=15) + + plt.ylabel(metric) + plt.yticks(np.arange(0, 110, 10)) + plt.ylim(48, 102) + plt.xticks(x_axis, models) + plt.xlabel("model name") + plt.legend( + bbox_to_anchor=(0, 1.0, 1, 0.2), + ncol=3, + mode="expand" + ) + + plt.tight_layout() + if save: + if 'png' not in save_file_name: + save_file_name = f"{save_file_name}.png" + save_path = os.path.join( + save_dir_path, + save_file_name + ) + print(f"saved fig in: {save_path}") + plt.savefig(fname=save_path, format='png') + if show: + plt.show() + plt.clf() + p_utils.set_params() + +def plot_all_models( + data_dict, + save_dir_path, + save = True, + show = False, + include_av = False +): + for model_name, model_data_dict in data_dict.items(): + model_plot( + model_data_dict=model_data_dict, + model_name=model_name, + save_dir_path=save_dir_path, + save=save, + show=show, + include_av=include_av + ) + +def fix_keys_metric_data_dict(metric_data_dict): + fixed_metric_keys = {} + for metric_name, metric_value in metric_data_dict.items(): + if "mPr@" in metric_name: + fixed_metric_keys[metric_name.split(".")[0]] = metric_value + else: + fixed_metric_keys[metric_name] = metric_value + return fixed_metric_keys + +def fix_metric_keys_model_data_dict(model_data_dict): + fixed_metric_keys = {} + for scene_name, scene_data in model_data_dict.items(): + fixed_metric_keys[scene_name] = fix_keys_metric_data_dict( + metric_data_dict=scene_data + ) + return fixed_metric_keys + +def fix_metric_keys_global_data_dict(data_dict): + fixed_metric_keys = {} + for model_name, model_data_dict in data_dict.items(): + fixed_metric_keys[model_name] = fix_metric_keys_model_data_dict( + model_data_dict=model_data_dict + ) + return fixed_metric_keys + + +def model_data_grouped_by_metric( + model_data_dict, + model_name, + include_av = False +): + grouped_dict = { + "mPr@50" : [], + "mPr@60" : [], + "mPr@70" : [], + "mPr@80" : [], + "mPr@90" : [], + "mIoU" : [] + } + for scene_name, scene_data in model_data_dict.items(): + if not include_av and scene_name == "overall": + continue + for metric_name, value in scene_data.items(): + if metric_name not in grouped_dict.keys(): + continue + grouped_dict[metric_name].append( + round(value, 1) + ) + + + return grouped_dict + +def get_relative_positions( + grouped_dict, + step_size = 1 +): + n_bars = len(list(grouped_dict.keys())) + # mid = 0 if n_bars % 2 == 0 else -1 + mid = 0 + + min_pos = mid - (n_bars/2 - step_size/2) + max_pos = mid + (n_bars/2 - step_size/2) + + relative_positions = np.arange(min_pos, max_pos + step_size, step_size) + return relative_positions + +def model_plot( + model_data_dict, + model_name, + save_dir_path, + save = True, + show = False, + include_av = False +): + + model_data_dict = fix_metric_keys_model_data_dict( + model_data_dict=model_data_dict + ) + + grouped_by_metric = model_data_grouped_by_metric( + model_data_dict=model_data_dict, + model_name=model_name, + include_av=include_av + ) + + p_utils.set_params( + param_dict=p_utils.scenes_plot_model + ) + + scenes = list(model_data_dict.keys()) + if not include_av and "overall" in scenes: + scenes.remove("overall") + scenes = tuple(scenes) + + x_axis = np.arange(len(scenes)) + + bar_width = 0.95 / len(grouped_by_metric.keys()) + + relative_positions = get_relative_positions( + grouped_dict=grouped_by_metric + ) + for item_idx, metric_item in enumerate(grouped_by_metric.items()): + (metric_name, metric_value) = metric_item + offset = (bar_width) * relative_positions[item_idx] + metric_name = p_utils.map_metric_key_plot(metric_name) + rects = plt.bar( + x_axis + offset, + metric_value, + bar_width, + label=metric_name + ) + plt.bar_label(rects, padding=3, fontsize=15) + + plt.ylabel("score (%)") + plt.yticks(np.arange(0, 110, 10)) + plt.ylim(0, 108) + plt.xticks(x_axis, scenes) + plt.xlabel("scene type") + plt.legend( + bbox_to_anchor=(0, 1.0, 1, 0.2), + ncol=len(grouped_by_metric.keys()), + mode="expand" + ) + + plt.tight_layout() + if save: + save_path = os.path.join( + save_dir_path, + f"{model_name}_scenes.png" + ) + print(f"saved fig in: {save_path}") + plt.savefig(fname=save_path, format='png') + if show: + plt.show() + plt.clf() + p_utils.set_params() + + + +def main(): + args = parse_args() + p_utils.set_params() + data_dict = get_data_dict(json_path=args.scene_results_json_path) + + if args.single_model: + model_name = get_model_dir_name( + scene__data_path=args.scene_results_json_path + ).split("_")[0] + model_plot( + model_data_dict=data_dict, + model_name=model_name, + save_dir_path=args.save_dir_path, + save=(not args.dont_save), + show=args.show + ) + + + if args.global_plot: + global_plot( + data_dict=data_dict, + save_dir_path=args.save_dir_path, + save_file_name="global", + save=(not args.dont_save), + show=args.show, + include_av=args.include_av, + plot_sep=args.plot_sep + ) + if args.per_model: + plot_all_models( + data_dict=data_dict, + save_dir_path=args.save_dir_path, + save=(not args.dont_save), + show=args.show, + include_av=args.include_av + + ) + p_utils.reset_params() + +if __name__ == '__main__': + main() diff --git a/my_projects/scripts/plot_train_acc.py b/my_projects/scripts/plot_train_acc.py new file mode 100644 index 0000000000..280f5bb0d1 --- /dev/null +++ b/my_projects/scripts/plot_train_acc.py @@ -0,0 +1,175 @@ +import subprocess +import os +import argparse +import plotting_utils as p_utils +import json + + + +MODEL_NAMES = [ + "bisenet", + "mask2former", + "maskformer", + "segformer", + "segnext" +] + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + '--selection_path', + type=str, + help='path of models', + default='my_projects/best_models/selection_trained/fine_tuning' + ) + parser.add_argument( + '--results_path', + '-rp', + type=str, + help='path to store results', + default='my_projects/training_data' + ) + parser.add_argument( + '--reduce_zeros', + '-rz', + action='store_true' + ) + parser.add_argument( + '--one_plot', + '-op', + action='store_true' + ) + args = parser.parse_args() + + + return args + +def get_json_path(model_dir_path): + json_path = None + for dir in os.listdir(model_dir_path): + dir_path = os.path.join( + model_dir_path, + dir + ) + if os.path.isdir(dir_path): + json_path = os.path.join( + dir_path, + "vis_data", + "scalars.json" + ) + if not os.path.exists(json_path): + json_path = None + break + return json_path + +def fix_jsons(model_dir_path): + json_dicts = [] + for dir in os.listdir(model_dir_path): + dir_path = os.path.join( + model_dir_path, + dir + ) + if os.path.isdir(dir_path): + if "vis_data" not in os.listdir(dir_path): + vis_data_path = os.path.join( + dir_path, "vis_data" + ) + os.mkdir(vis_data_path) + log_file = [ + log_file for log_file in os.listdir(dir_path) + if ".log" in log_file + ][0] + log_path = os.path.join(dir_path, log_file) + with open(log_path, 'r') as file_ptr: + current_train_iter = 0 + for line in file_ptr: + if "Iter(train) [" in line: + print("iter") + current_train_iter = line.split('[', 1)[1].split( + ']')[0] + + current_train_iter = int( + current_train_iter.split("/")[0] + ) + print(current_train_iter) + # current_train_iter = int( + # re.search(r"\[(\w+\]", line) + # ) + if "Iter(val)" in line: + miou = float(line.split("mIoU: ")[-1].split()[0]) + + json_dicts.append( + { + "step" : current_train_iter, + "mIoU" : miou + } + ) + vis_data_path = os.path.join( + dir_path, "vis_data" + ) + scalars_path = os.path.join(vis_data_path, "scalars.json") + with open(scalars_path, 'a') as scal_file: + for json_dict in json_dicts: + json.dump(json_dict, scal_file) + scal_file.write("\n") + + + + +def main(): + args = parse_args() + # for model_name in MODEL_NAMES: + # jsons = {} + # for dataset_name in os.listdir(args.selection_path): + # dataset_path = os.path.join(args.selection_path, dataset_name) + # for model_dir_name in os.listdir(dataset_path): + # if model_name in model_dir_name: + # model_dir_path = os.path.join(dataset_path, model_dir_name) + # json_path = get_json_path(model_dir_path=model_dir_path) + # print(f"json_pth = {json_path}") + # if json_path is None: + # print("fix json") + # fix_jsons(model_dir_path=model_dir_path) + + for model_name in MODEL_NAMES: + jsons = {} + for dataset_name in os.listdir(args.selection_path): + dataset_path = os.path.join(args.selection_path, dataset_name) + for model_dir_name in os.listdir(dataset_path): + if model_name in model_dir_name: + model_dir_path = os.path.join(dataset_path, model_dir_name) + json_path = get_json_path(model_dir_path=model_dir_path) + if json_path is None: + print(f"no json found in {model_dir_path}") + continue + jsons[dataset_name] = json_path + call_list = [ + "python", + "my_projects/scripts/analyze_logs.py" + ] + for json_path in jsons.values(): + call_list.append(json_path) + call_list.append("--keys") + call_list.append("mIoU") + # call_list.append("--title") + # call_list.append(f"{model_name}_learning") + + call_list.append("--legend") + for dataset_name in jsons.keys(): + call_list.append(p_utils.map_dataset_name(dataset_name)) + + save_path = os.path.join( + args.results_path, + f"{model_name}_learning.png" + ) + call_list.append("--out") + call_list.append(save_path) + + if args.reduce_zeros: + call_list.append('-rz') + + subprocess.call(call_list) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/my_projects/scripts/plotting_utils.py b/my_projects/scripts/plotting_utils.py new file mode 100644 index 0000000000..f234b62476 --- /dev/null +++ b/my_projects/scripts/plotting_utils.py @@ -0,0 +1,232 @@ + +from matplotlib import rcParams, rcParamsDefault + +double_col_readable = { + 'figure.figsize' : (12, 8), + 'legend.fontsize' : 25, + 'axes.labelsize' : 25, + 'axes.titlesize' : 25, + 'xtick.labelsize' : 25, + 'ytick.labelsize' : 25, + 'lines.linewidth' : 6 +} + +scenes_plot_global = { + 'figure.figsize' : (16, 8), + 'legend.fontsize' : 30, + 'axes.labelsize' : 30, + 'axes.titlesize' : 30, + 'xtick.labelsize' : 28, + 'ytick.labelsize' : 25, + 'lines.linewidth' : 6 +} + +scenes_plot_global_per_scene_type = { + 'figure.figsize' : (12, 8), + 'legend.fontsize' : 30, + 'axes.labelsize' : 30, + 'axes.titlesize' : 30, + 'xtick.labelsize' : 20, + 'ytick.labelsize' : 25, + 'lines.linewidth' : 6 +} +scenes_plot_model = { + 'figure.figsize' : (18, 7), + 'legend.fontsize' : 25, + 'axes.labelsize' : 30, + 'axes.titlesize' : 30, + 'xtick.labelsize' : 30, + 'ytick.labelsize' : 25, + 'lines.linewidth' : 6 +} + +clutter_plot = { + 'figure.figsize' : (12, 8), + 'legend.fontsize' : 25, + 'axes.labelsize' : 30, + 'axes.titlesize' : 30, + 'xtick.labelsize' : 25, + 'ytick.labelsize' : 25, + 'lines.linewidth' : 8 +} +LEARNING_PLOT_PARAMS = { + 'figure.figsize' : (12, 8), + 'legend.fontsize' : 25, + 'axes.labelsize' : 30, + 'axes.titlesize' : 30, + 'xtick.labelsize' : 25, + 'ytick.labelsize' : 25, + 'lines.linewidth' : 8 +} +TRADEOFF_PLOT_PARAMS = { + 'figure.figsize' : (12, 8), + 'legend.fontsize' : 25, + 'axes.labelsize' : 30, + 'axes.titlesize' : 30, + 'xtick.labelsize' : 25, + 'ytick.labelsize' : 25, + 'lines.linewidth' : 8, + 'lines.markersize' : 30 +} +PRED_VISUALIZATION_FIGURE_PARAMS_2ROWS = { + 'figure.figsize' : (16, 8), + 'figure.constrained_layout.use' : True, + 'legend.fontsize' : 20, + # 'axes.labelsize' : 30, + 'axes.titlesize' : 25, + # 'xtick.labelsize' : 30, + # 'ytick.labelsize' : 25, + 'lines.linewidth' : 6 +} +PRED_VISUALIZATION_FIGURE_PARAMS_3ROWS = { + 'figure.figsize' : (10, 6), + 'figure.constrained_layout.use' : True, + 'legend.fontsize' : 20, + # 'axes.labelsize' : 30, + 'axes.titlesize' : 30, + # 'xtick.labelsize' : 30, + # 'ytick.labelsize' : 25, + 'lines.linewidth' : 4 +} +PRED_VISUALIZATION_FIGURE_PARAMS = { + 'figure.figsize' : (16, 4), + 'figure.constrained_layout.use' : True, + 'legend.fontsize' : 20, + # 'axes.labelsize' : 30, + 'axes.titlesize' : 12, + # 'xtick.labelsize' : 30, + # 'ytick.labelsize' : 25, + 'lines.linewidth' : 6 +} +MODEL_COLORS = { + "BiSeNet" : 'tab:blue', + "Mask2Former" : 'tab:orange', + "MaskFormer" : 'tab:green', + "SegFormer" : 'tab:red', + "SegNeXt" : 'tab:purple' +} + +def set_params(param_dict=None): + if param_dict is None: + param_dict = double_col_readable + param_dict_ = {} + for key, val in param_dict.items(): + if key in rcParams.keys(): + param_dict_[key] = val + else: + print(f"key {key} not in rcParams") + rcParams.update( + param_dict_ + ) + +def reset_params(): + rcParams.update(rcParamsDefault) + +# def fix_metric_name(metric_name): +# return metric_name.split(".")[0] + +METRIC_KEY_MAP = { + "mPr@50" : "mPr@50", + "mPr@60" : "mPr@60", + "mPr@70" : "mPr@70", + "mPr@80" : "mPr@80", + "mPr@90" : "mPr@90", + "mPr@50.0" : "mPr@50", + "mPr@60.0" : "mPr@60", + "mPr@70.0" : "mPr@70", + "mPr@80.0" : "mPr@80", + "mPr@90.0" : "mPr@90", + "mIoU" : "mIoU", + "average_fps" : "FPS", + "average_mem" : "Mem (MB)", + "FPS" : "FPS", + "Mem (MB)" : "Mem (MB)" +} + +def map_metric_key_strict(key): + if key in METRIC_KEY_MAP.keys(): + return METRIC_KEY_MAP[key] + return False + +def map_metric_key(key): + if key in METRIC_KEY_MAP.keys(): + return METRIC_KEY_MAP[key] + return key + # return key.split(".")[0] + + +METRIC_KEY_MAP_PLOT = { + "mPr@50" : "$\mathregular{mPr_{50}}$", + "mPr@60" : "$\mathregular{mPr_{60}}$", + "mPr@70" : "$\mathregular{mPr_{70}}$", + "mPr@80" : "$\mathregular{mPr_{80}}$", + "mPr@90" : "$\mathregular{mPr_{90}}$", + "mPr@50.0" : "$\mathregular{mPr_{50}}$", + "mPr@60.0" : "$\mathregular{mPr_{60}}$", + "mPr@70.0" : "$\mathregular{mPr_{70}}$", + "mPr@80.0" : "$\mathregular{mPr_{80}}$", + "mPr@90.0" : "$\mathregular{mPr_{90}}$" +} + +def map_metric_key_plot(key): + if key in METRIC_KEY_MAP_PLOT.keys(): + return METRIC_KEY_MAP_PLOT[key] + return key + +MODEL_NAME_MAP = { + "bisenet" : "BiSeNet", + "bisenetv1" : "BiSeNet", + "mask2former" : "Mask2Former", + "maskformer" : "MaskFormer", + "segformer" : "SegFormer", + "segnext" : "SegNeXt", +} + +def map_model_name(model_name): + if model_name.lower() in MODEL_NAME_MAP.keys(): + return MODEL_NAME_MAP[model_name.lower()] + return model_name + +DATASET_NAME_MAP = { + "hots" : "HOTS", + "hots cat" : "HOTS-C", + "irl vision" : "SOD", + "irl vision cat" : "SOD-C", + "arid20" : "ARID20", + "arid10" : "ARID10", + "sodhots" : "SODHOTS-C", + "sodhots-c" : "SODHOTS-C", + "HOTS" : "HOTS", + "HOTS-C" : "HOTS-C", + "SOD" : "SOD", + "SOD-C" : "SOD-C", + "ARID20" : "ARID20", + "ARID10" : "ARID10", + "SODHOTS-C" : "SODHOTS-C" +} + +def map_dataset_name(ds_name): + if ds_name in DATASET_NAME_MAP.keys(): + return DATASET_NAME_MAP[ds_name] + ds_name_ = ds_name.lower() + if ds_name_ in DATASET_NAME_MAP.keys(): + return DATASET_NAME_MAP[ds_name_] + if "sodhots" in ds_name_: + return DATASET_NAME_MAP["sodhots"] + if "hots" in ds_name_: + if "cat" in ds_name_ or "c" in ds_name_: + return DATASET_NAME_MAP["hots cat"] + else: + return DATASET_NAME_MAP["hots"] + if "irl" in ds_name_: + if "cat" in ds_name_: + + return DATASET_NAME_MAP["irl vision cat"] + else: + return DATASET_NAME_MAP["irl vision"] + if "arid20" in ds_name_: + return DATASET_NAME_MAP["arid20"] + + if "arid10" in ds_name_: + return DATASET_NAME_MAP["arid10"] + return ds_name \ No newline at end of file diff --git a/my_projects/scripts/visualize_preds.py b/my_projects/scripts/visualize_preds.py new file mode 100644 index 0000000000..5e50a5fc5e --- /dev/null +++ b/my_projects/scripts/visualize_preds.py @@ -0,0 +1,684 @@ +import argparse +import os +from copy import copy +import numpy as np +from PIL import Image +from matplotlib import pyplot as plt +import matplotlib.lines as mlines +import plotting_utils as p_utils +from mmengine.structures import PixelData +from mmseg.utils import ( + hots_v1_classes, + hots_v1_palette, + irl_vision_sim_classes, + irl_vision_sim_palette, + ade_classes, + ade_palette, + hots_v1_cat_classes, + hots_v1_cat_palette, + irl_vision_sim_cat_classes, + irl_vision_sim_cat_palette, + arid20cat_classes, + arid20cat_palette, + arid10cat_classes, + arid10cat_palette, + sodhots_c_classes, + sodhots_c_palette, + cocostuff_classes, + cocostuff_palette +) +from mmseg.visualization.local_visualizer_custom import SegLocalVisualizerCustom as SegVis + +from math import ceil + +# TODO temp, could be taken from testloaders etc +DATASET_TEST_IMG_PATH = { + "HOTS" : "/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/img_dir/test", + "HOTS-C" : "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/img_dir/test", + "SOD" : "/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/img_dir/test", + "SOD-C" : "/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/img_dir/test", + "ARID20" : "/media/ids/Ubuntu files/data/ARID20_CAT/img_dir/test", + "ARID10" : "/media/ids/Ubuntu files/data/ARID10_CAT/img_dir/test", + "SODHOTS-C" : "/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/img_dir/test" +} + +DATASET_TEST_ANN_PATH = { + "HOTS" : "/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/ann_dir/test", + "HOTS-C" : "/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/ann_dir/test", + "SOD" : "/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/ann_dir/test", + "SOD-C" : "/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/ann_dir/test", + "ARID20" : "/media/ids/Ubuntu files/data/ARID20_CAT/ann_dir/test", + "ARID10" : "/media/ids/Ubuntu files/data/ARID10_CAT/ann_dir/test", + "SODHOTS-C" : "/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/ann_dir/test" +} + +DATASET_PALETTE = { + "HOTS" : hots_v1_palette, + "HOTS-C" : hots_v1_cat_palette, + "SOD" : irl_vision_sim_palette, + "SOD-C" : irl_vision_sim_cat_palette, + "ARID20" : arid20cat_palette, + "ARID10" : arid10cat_palette, + "ADE20K" : ade_palette, + "SODHOTS-C" : sodhots_c_palette +} + +DATASET_CLASSES = { + "HOTS" : hots_v1_classes, + "HOTS-C" : hots_v1_cat_classes, + "SOD" : irl_vision_sim_classes, + "SOD-C" : irl_vision_sim_cat_classes, + "ARID20" : arid20cat_classes, + "ARID10" : arid10cat_classes, + "ADE20K" : ade_classes, + "SODHOTS-C" : sodhots_c_classes +} + +DATASET_DEFAULT_PRED_LIST = { + "HOTS" : [ + "kitchen_6_top_raw_0.png", + "mix_2_top_raw_8.png", + "office_7_top_raw_4.png", + "table_8_top_raw_0.png" + ], + "HOTS-C" : [ + "kitchen_6_top_raw_0.png", + "mix_2_top_raw_8.png", + "office_7_top_raw_4.png", + "table_8_top_raw_0.png" + ], + "SOD" : [ + "scene_75.png", + "scene_99.png", + "scene_109.png", + "scene_273.png" + ], + "SOD-C" : [ + "scene_75.png", + "scene_99.png", + "scene_109.png", + "scene_273.png" + ], + "ARID20" : [ + "floor_bottom_seq03_17.png", + "table_bottom_seq01_19.png", + "table_top_seq02_16.png", + "table_top_seq06_16.png" + ], + "ARID10" : [ + "floor_bottom_box_seq33_5.png", + "floor_top_non-fruits_seq39_7.png", + "table_bottom_mixed_seq13_5.png", + "table_top_non-fruits_seq21_3.png" + ], + "ARID20_CLUTTER" : [ + "table_top_seq03_7.png", + "table_top_seq03_14.png", + "table_top_seq03_19.png" + ], + "SODHOTS-C" : [ + "kitchen_8_top_raw_0.png", + "mix_2_top_raw_6.png", + "office_5_top_raw_2.png", + "mix_1_top_raw_17.png" + ] +} + +CONVERSION_IDX = 255 +CONVERSION_COLOR = [255, 255, 255] +CONVERSION_LABEL = "unknown" + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + 'results_path', + help='dir containing model dirs', + type=str + ) + parser.add_argument( + 'dataset_name', + type=str + ) + parser.add_argument( + '--pred_names', + '-pn', + type=str, + nargs='+', + default=[] + ) + + parser.add_argument( + '--save_path', + '-sp', + type=str, + default="my_projects/images_plots/prediction_images" + ) + parser.add_argument( + '--set_name', + '-sn', + type=str, + default=None + ) + parser.add_argument( + '--save_separate', + '-sep', + action='store_true' + ) + parser.add_argument( + '--show', + action='store_true' + ) + parser.add_argument( + '--legend_mode', + choices=[ + "none", "full", + "interesting", "separate", + "gt_only" + ], + default="interesting" + ) + parser.add_argument( + '--legend_pos', + choices=[ + "side", "low" + ], + default="side" + ) + parser.add_argument( + '--ignore_background', + '-ibg', + action='store_true' + ) + parser.add_argument( + '--alpha', + '-a', + type=float, + default=0.5 + ) + parser.add_argument( + '--include_label', + '-il', + action='store_true' + ) + parser.add_argument( + '--label_scale', + '-ls', + type=float, + default=0.04 + ) + parser.add_argument( + '--include_raw', + '-raw', + action='store_true' + ) + parser.add_argument( + '--include_gt', + '-gt', + action='store_true' + ) + parser.add_argument( + '--clutter', + '-cl', + action='store_true' + ) + parser.add_argument( + '--test_dataset', + '-tds', + type=str, + default=None + ) + parser.add_argument( + '--output_dataset', + '-ods', + type=str, + default=None + ) + # parser.add_argument( + # '--with_iou', + # '-iou', + # action='store_true' + # ) + + args = parser.parse_args() + # # TODO temp defaults + # args.include_raw = True + # args.include_gt = True + + args.dataset_name = p_utils.map_dataset_name(args.dataset_name) + if args.test_dataset is None: + args.test_dataset = args.dataset_name + else: + args.test_dataset = p_utils.map_dataset_name(args.test_dataset) + + if args.output_dataset is None: + args.output_dataset = args.dataset_name + else: + args.output_dataset = p_utils.map_dataset_name(args.output_dataset) + + if not args.pred_names: + ds_name = args.test_dataset + if ds_name == "ARID20" and args.clutter: + ds_name = "ARID20_CLUTTER" + args.pred_names = DATASET_DEFAULT_PRED_LIST[ds_name] + if not args.set_name: + pred_name = args.pred_names[0] if len(args.pred_names) == 1 else "set" + if ds_name == "ARID20" and args.clutter: + pred_name = f"clutter_{pred_name}" + pred_name = f"_{pred_name}" + args.set_name = f"{args.dataset_name}{pred_name}".replace( + ".png", "" + ) + print("args") + print(args) + print() + return args + + +def get_gt_img(test_dataset, pred_name): + gt_path = os.path.join( + DATASET_TEST_ANN_PATH[test_dataset], + pred_name + ) + try: + gt_img = np.array(Image.open(gt_path)).astype(np.uint8) + return gt_img + except: + print(f"failed to open gt image at {gt_path}") + return None + +def get_pred_label(pred_name, model_dir_path): + pred_label_path = os.path.join( + model_dir_path, + "pred_results", + pred_name + ) + try: + pred_label_img = np.array(Image.open(pred_label_path)).astype(np.uint8) + return pred_label_img + except: + print(f"failed to open pred image at {pred_label_path}") + return None + +def get_rgb_img(test_dataset, pred_name): + rgb_path = os.path.join( + DATASET_TEST_IMG_PATH[test_dataset], + pred_name + ) + try: + rgb_img = np.array(Image.open(rgb_path)) + return rgb_img + except Exception as ex: + print(f"failed to open rgb image at {rgb_path}\n{ex}") + return None + +def label_to_pixeldata(label: np.ndarray) -> PixelData: + return PixelData(data=label) + +def get_model_pred_labels(results_path, pred_name): + pred_dict = {} + for model_dir_name in os.listdir(results_path): + if model_dir_name == "data": + continue + model_name = p_utils.map_model_name( + model_name=model_dir_name.split("_")[0] + ) + model_dir_path = os.path.join( + results_path, + model_dir_name + ) + pred_label = get_pred_label(pred_name=pred_name, model_dir_path=model_dir_path) + pred_dict[model_name] = pred_label + return pred_dict + +def get_all_figures_dict(args): + figures_dict = {} + for pred_name in args.pred_names: + figures_dict[pred_name] = {} + if args.include_raw: + figures_dict[pred_name]["Input"] = get_rgb_img( + test_dataset=args.test_dataset, + pred_name=pred_name + ) + if args.include_gt: + figures_dict[pred_name]["Ground Truth"] = get_gt_img( + test_dataset=args.test_dataset, + pred_name=pred_name + ) + + pred_dict = get_model_pred_labels( + results_path=args.results_path, + pred_name=pred_name + ) + for model_name, model_data in pred_dict.items(): + figures_dict[pred_name][model_name] = model_data + + return figures_dict + + + +def get_ids_interesting(figures_dict): + gt_label = figures_dict["Ground Truth"] + gt_label_ids = [] + pred_label_ids = [] + for fig_name, pred_label in figures_dict.items(): + mismatch_pred = pred_label[pred_label != gt_label] + mismatch_gt = gt_label[gt_label != pred_label] + for pred_id in np.unique(mismatch_pred): + if len(mismatch_pred[mismatch_pred == pred_id]) < 10: + continue + if pred_id not in pred_label_ids: + pred_label_ids.append(pred_id) + for gt_id in np.unique(mismatch_gt): + if len(mismatch_gt[mismatch_gt == gt_id]) < 10: + continue + if gt_id not in gt_label_ids: + gt_label_ids.append(gt_id) + pred_label_ids = [ + pred_id for pred_id in pred_label_ids + if pred_id not in gt_label_ids + ] + if 0 in gt_label_ids: + gt_label_ids = sorted(gt_label_ids)[1:] + + + return gt_label_ids, pred_label_ids + +def get_ids_full(figures_dict): + gt_label_ids = np.unique(figures_dict["Ground Truth"]) + pred_label_ids = [] + for fig_name, pred_label in figures_dict.items(): + for pred_id_ in np.unique(pred_label): + # skip very small areas + if len(pred_label[pred_label == pred_id_]) < 10: + continue + if pred_id_ not in gt_label_ids and pred_id_ not in pred_label_ids: + pred_label_ids.append(pred_id_) + + return gt_label_ids, pred_label_ids + +def get_ids_gt_only(figures_dict): + return np.unique(figures_dict["Ground Truth"]), [] + + + +def generate_set_figure(args): + p_utils.reset_params() + figures_dict = get_all_figures_dict(args=args) + n_rows = len(figures_dict.keys()) + n_cols = len(tuple(figures_dict.items())[0][1].keys()) + # set params + param_dict = copy(p_utils.PRED_VISUALIZATION_FIGURE_PARAMS) + # (fig_w, fig_h) = param_dict['figure.figsize'] + # fig_h *= n_rows + param_dict['figure.figsize'] = (2*n_cols, 1.6*n_rows) + p_utils.set_params(param_dict=param_dict) + + figure = plt.figure() + subplots = figure.subplots( + nrows=n_rows, + ncols=n_cols, + gridspec_kw = { + 'wspace':0.01, + 'hspace':0.001 + } + ) + print(f"drawing and labeling using {args.output_dataset}") + classes = copy(DATASET_CLASSES[args.output_dataset]()) + palette = copy(DATASET_PALETTE[args.output_dataset]()) + print(f"getting ann and rgb from {args.test_dataset}") + visualizer = SegVis(alpha=args.alpha) + + for row_idx, (pred_name, pred_dict) in enumerate(figures_dict.items()): + figures_item_list = list(pred_dict.items()) + rgb_img = get_rgb_img( + test_dataset=args.test_dataset, + pred_name=pred_name + ) + if rgb_img is None: + return + for col_idx in range(n_cols): + (fig_name, label) = figures_item_list.pop(0) + if n_rows == 1: + axes = subplots[col_idx] + else: + axes = subplots[row_idx][col_idx] + if not fig_name == "Input": + classes_ = classes + palette_ = palette + if "bise" in fig_name.lower() and args.output_dataset == "ADE20K": + classes_ = cocostuff_classes() + palette_ = cocostuff_palette() + img = visualizer._draw_sem_seg( + image=rgb_img.copy(), + sem_seg=label_to_pixeldata(label=label), + classes=classes_, + palette=palette_, + with_labels=args.include_label, + label_scale=args.label_scale, + ignore_idx=0, + ignore_background=args.ignore_background + ) + else: + img = label + axes.imshow(img) + axes.axis('off') + if row_idx == 0: + axes.set_title(fig_name) + plt.subplots_adjust(hspace=0) + figure.tight_layout() + save_path = os.path.join( + args.save_path, + f"{args.set_name}.png" + ) + print(f"saved fig in : {save_path}") + figure.savefig( + save_path, + dpi=150, + bbox_inches='tight' + ) + + +def main(): + args = parse_args() + generate_set_figure(args=args) + +if __name__ == '__main__': + main() + + + + + + + + +# def generate_set_figure_old(args): +# p_utils.reset_params() +# p_utils.set_params(param_dict=p_utils.PRED_VISUALIZATION_FIGURE_PARAMS_2ROWS) +# rgb_img = get_rgb_img(args=args) +# if rgb_img is None: +# return +# figure = plt.figure() +# subplots = figure.subplots( +# nrows=2, +# ncols=3, +# gridspec_kw = { +# 'wspace':0.05, +# 'hspace':0.20 +# } +# ) +# figures_dict = get_all_figures_dict(args=args) +# classes = copy(DATASET_CLASSES[args.dataset_name]()) +# palette = copy(DATASET_PALETTE[args.dataset_name]()) + + +# figures_item_list = list(figures_dict.items()) +# visualizer = SegVis(alpha=args.alpha) +# for row_idx in range(len(subplots)): +# for col_idx in range(len(subplots[row_idx])): +# (fig_name, label) = figures_item_list.pop(0) +# axes = subplots[row_idx][col_idx] + + +# img = visualizer._draw_sem_seg( +# image=rgb_img.copy(), +# sem_seg=label_to_pixeldata(label=label), +# classes=classes, +# palette=palette, +# with_labels=False +# ) +# axes.imshow(img) +# axes.axis('off') +# axes.set_title(fig_name) +# if ( +# args.legend_mode == "full" +# or +# args.legend_mode == "interesting" +# or +# args.legend_mode == "gt_only" + +# ): +# legend_handles = generate_legend_handles( +# figures_dict=figures_dict, +# classes=classes, +# palette=palette, +# legend_mode=args.legend_mode, +# ignore_background=args.ignore_background +# ) +# fontsize = min( +# max( +# 300/len(legend_handles), +# 15 +# ), +# 20 +# ) +# if args.legend_pos == "low": +# ncols = 4 +# bbheight = ceil(len(legend_handles) / ncols) +# bby = -1 * ((bbheight * 2) + (bbheight / 2.0)) / 100 +# print(bbheight) +# print(bby) + +# figure.legend( +# handles=legend_handles, +# # bbox_to_anchor=(0.12, 0.72, 1, 0.2), +# bbox_to_anchor=(0.5, bby), +# ncols=ncols, +# loc = 'lower center', +# fontsize=fontsize +# ) +# else: +# figure.legend( +# handles=legend_handles, +# bbox_to_anchor=(0.12, 0.72, 1, 0.2), +# fontsize=fontsize +# ) +# figure.tight_layout() +# save_path = os.path.join( +# args.save_path, +# f"{args.set_name}.png" +# ) +# print(f"saved fig in : {save_path}") +# figure.savefig( +# save_path, +# dpi=100, +# bbox_inches='tight' +# ) +# if args.legend_mode == "separate": +# figure.clf() +# figure = plt.figure(figsize=(8,8)) +# legend_handles = generate_legend_handles( +# figures_dict=figures_dict, +# classes=classes, +# palette=palette, +# legend_mode="full", +# ignore_background=args.ignore_background +# ) +# figure.legend(handles=legend_handles, ncols=2) +# figure.savefig( +# os.path.join( +# args.save_path, +# f"{args.set_name}_legend.png" +# ), +# dpi=100, +# bbox_inches='tight' +# ) + +# def generate_legend_handles( +# figures_dict, +# classes, +# palette, +# legend_mode, +# ignore_background +# ): +# gt_label_ids = [] +# pred_label_ids = [] +# if legend_mode == "full": +# gt_label_ids, pred_label_ids = get_ids_full( +# figures_dict=figures_dict +# ) +# if legend_mode == "interesting": +# gt_label_ids, pred_label_ids = get_ids_interesting( +# figures_dict=figures_dict +# ) +# if legend_mode == "gt_only": +# gt_label_ids, pred_label_ids = get_ids_gt_only( +# figures_dict=figures_dict +# ) + +# legend_handles = [] + +# for gt_label_id in gt_label_ids: +# if gt_label_id == CONVERSION_IDX: +# class_name = CONVERSION_LABEL +# class_color = CONVERSION_COLOR +# else: +# class_name = classes[gt_label_id] +# class_color = palette[gt_label_id] +# if class_name[0] == "_": +# class_name = class_name[1:] +# if ignore_background and "background" in class_name: +# continue + + +# class_color = tuple([color/255 for color in class_color]) +# legend_handles.append( +# mlines.Line2D( +# [], [], +# color=class_color, +# label=class_name, +# linestyle='-', +# linewidth=10 +# ) +# ) +# if pred_label_ids: +# legend_handles.append( +# mlines.Line2D( +# [], [], +# color='k', +# label="pred not present", +# marker='o', +# linewidth=0, +# markersize=16 +# ) +# ) +# for pred_label_id in pred_label_ids: + +# if pred_label_id == CONVERSION_IDX: +# class_name = CONVERSION_LABEL +# class_color = CONVERSION_COLOR +# else: +# class_name = classes[pred_label_id] +# class_color = palette[pred_label_id] +# class_color = tuple([color/255 for color in class_color]) +# legend_handles.append( +# mlines.Line2D( +# [], [], +# color=class_color, +# label=class_name, +# marker='o', +# linewidth=0, +# markersize=16 +# ) +# ) +# return legend_handles \ No newline at end of file diff --git a/my_projects/selection_data/model_data_mmlab b/my_projects/selection_data/model_data_mmlab new file mode 100644 index 0000000000..371cc645b8 --- /dev/null +++ b/my_projects/selection_data/model_data_mmlab @@ -0,0 +1,136 @@ +config : configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py +Mem (GB) : 6.33 +Inf time (fps) : 74.24 +mIoU : 28.55 + + +config : configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py +Mem (GB) : 9.28 +Inf time (fps) : 32.6 +mIoU : 34.88 + + +config : configs/pspnet/pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py +Mem (GB) : 9.6 +Inf time (fps) : 20.5 +mIoU : 40.53 + + +config : configs/deeplabv3/deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py +Mem (GB) : 9.6 +Inf time (fps) : 10.8 +mIoU : 41.37 + + + +config : configs/pspnet/pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512.py +Mem (GB) : 9.6 +Inf time (fps) : 20.5 +mIoU : 36.33 + + +config : configs/deeplabv3/deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512.py +Mem (GB) : 9.6 +Inf time (fps) : 10.8 +mIoU : 35.73 + + +config : configs/mask2former/mask2former_swin-t_8xb2-160k_ade20k-512x512.py +Mem (GB) : 3.826 +Inf time (fps) : 23.82 +mIoU : 48.66 + + +config : configs/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py +Mem (GB) : 3.73 +Inf time (fps) : 100.0 +mIoU : 46.69 + + +config : configs/segmenter/segmenter_vit-t_mask_8xb1-160k_ade20k-512x512.py +Mem (GB) : 1.21 +Inf time (fps) : 27.98 +mIoU : 39.99 + + +config : configs/segnext/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py +Mem (GB) : 17.88 +Inf time (fps) : 52.38 +mIoU : 41.5 + + +config : configs/segformer/segformer_mit-b0_8xb2-160k_ade20k-512x512.py +Mem (GB) : 2.1 +Inf time (fps) : 51.32 +mIoU : 37.41 + + +config : configs/hrnet/fcn_hr18s_4xb4-40k_voc12aug-512x512.py +Mem (GB) : 1.8 +Inf time (fps) : 43.36 +mIoU : 66.61 + + +config : configs/ocrnet/ocrnet_hr18s_4xb4-40k_voc12aug-512x512.py +Mem (GB) : 3.5 +Inf time (fps) : 31.55 +mIoU : 72.76 + + +config : configs/deeplabv3/deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512.py +Mem (GB) : 9.6 +Inf time (fps) : 9.81 +mIoU : 78.7 + + +config : configs/hrnet/fcn_hr18_4xb4-40k_voc12aug-512x512.py +Mem (GB) : 2.9 +Inf time (fps) : 23.48 +mIoU : 72.9 + + +config : configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480.py +Mem (GB) : 8.8 +Inf time (fps) : 9.68 +mIoU : 52.47 + + +config : configs/hrnet/fcn_hr48_4xb4-80k_pascal-context-59-480x480.py +Mem (GB) : 6.1 +Inf time (fps) : 8.86 +mIoU : 51.12 + +config : configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480.py +Mem (GB) : 9.2 +Inf time (fps) : 7.09 +mIoU : 52.61 + + +config : configs/pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes.py +Mem (GB) : 5.14 +Inf time (fps) : 71.98 +mIoU : 80.22 + +config : configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024.py +Mem (GB) : 1.7 +Inf time (fps) : 85.85 +mIoU : 77.84 + + +config : configs/pidnet/pidnet-l_2xb6-120k_1024x1024-cityscapes.py +Mem (GB) : 5.83 +Inf time (fps) : 60.06 +mIoU : 80.89 + + +config : configs/pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes.py +Mem (GB) : 3.38 +Inf time (fps) : 80.82 +mIoU : 78.74 + + +config : configs/hrnet/fcn_hr18_4xb2-160k_cityscapes-512x1024.py +Mem (GB) : 2.9 +Inf time (fps) : 12.97 +mIoU : 78.8 + diff --git a/my_projects/selection_data/model_scores_on_hotsv1 b/my_projects/selection_data/model_scores_on_hotsv1 new file mode 100644 index 0000000000..7dce1bd7f1 --- /dev/null +++ b/my_projects/selection_data/model_scores_on_hotsv1 @@ -0,0 +1,1738 @@ + +chosen: + +twins_svt-b_fpn_fpnhead_1xb2-pre-ade20k-1k_hots-v1-512x512 : 2 +fcn_hr48_1xb2-1k_hots-v1-512x512 : 3 +knet-s3_r50-d8_fcn_1xb2-1k_hots-v1-512x512 : 8 +fpn_poolformer_m36_1xb2-pre-ade20k-1k_hots-v1-512x512 : 10 +mask2former_r50_1xb2-pre-ade20k-1k_hots-v1-512x512 : 10 + + + +required keys: dict_keys(['project', 'file_path', 'aAcc', 'mIoU', 'mAcc', 'mDice', 'mFscore', 'mPrecision', 'mRecall', 'map25', 'map50', 'map75', 'data_time', 'time']) +remove_list: + [{'project': 'fcn-d6_r101b-d16_1xb2-1k_hots-v1-512x512', 'file_path': 'fcn-d6_r101b-d16_1xb2-1k_hots-v1-512x512/iter_500.pth/20240617_225026/20240617_225026.log', '2024/06/1': '22:50:39', '': 'Load', 'checkpoin': 'from'}, {'project': 'fcn-d6_r101-d16_1xb2-1k_hots-v1-512x512', 'file_path': 'fcn-d6_r101-d16_1xb2-1k_hots-v1-512x512/iter_500.pth/20240617_223731/20240617_223731.log', '': '18.9800', 'map50': '15.0900', 'map75': '11.4000', 'data_time': '0.0072', 'time': '10.6327'}] + +############################################################ +############################################################ +############################################################ +############################################################ + +METRIC: aAcc +############################################################ +######################################## +project : fpn_poolformer_m48_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : fpn_poolformer_m48_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_033038/20240618_033038.log +aAcc : 97.73 +mIoU : 64.68 +mAcc : 68.45 +mDice : 69.82 +mFscore : 88.69 +mPrecision : 94.32 +mRecall : 68.45 +map25 : 57.4 +map50 : 56.2 +map75 : 49.43 +data_time : 0.0061 +time : 10.6644 +######################################## +project : mask2former_r101_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : mask2former_r101_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_054932/20240618_054932.log +aAcc : 97.61 +mIoU : 66.9 +mAcc : 76.76 +mDice : 76.35 +mFscore : 78.01 +mPrecision : 81.7 +mRecall : 76.76 +map25 : 56.06 +map50 : 51.5 +map75 : 42.51 +data_time : 0.0058 +time : 10.6307 +######################################## +project : fpn_poolformer_m36_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : fpn_poolformer_m36_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_032407/20240618_032407.log +aAcc : 97.4 +mIoU : 56.15 +mAcc : 61.68 +mDice : 61.54 +mFscore : 85.07 +mPrecision : 89.88 +mRecall : 61.68 +map25 : 52.84 +map50 : 48.67 +map75 : 42.21 +data_time : 0.0056 +time : 10.6328 +######################################## +project : mask2former_swin-t_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : mask2former_swin-t_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_060853/20240618_060853.log +aAcc : 97.31 +mIoU : 64.22 +mAcc : 74.04 +mDice : 74.42 +mFscore : 76.04 +mPrecision : 81.88 +mRecall : 74.04 +map25 : 53.13 +map50 : 48.36 +map75 : 41.15 +data_time : 0.0058 +time : 10.6339 +######################################## +project : mask2former_r50_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : mask2former_r50_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_055912/20240618_055912.log +aAcc : 97.26 +mIoU : 64.46 +mAcc : 75.81 +mDice : 75.04 +mFscore : 76.67 +mPrecision : 82.08 +mRecall : 75.81 +map25 : 58.36 +map50 : 51.44 +map75 : 42.62 +data_time : 0.0062 +time : 10.6537 +######################################## +project : fpn_poolformer_s36_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : fpn_poolformer_s36_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_035000/20240618_035000.log +aAcc : 96.81 +mIoU : 54.71 +mAcc : 60.79 +mDice : 61.22 +mFscore : 82.21 +mPrecision : 89.3 +mRecall : 60.79 +map25 : 51.47 +map50 : 47.6 +map75 : 37.87 +data_time : 0.0057 +time : 10.6299 +######################################## +project : fpn_poolformer_s24_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : fpn_poolformer_s24_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_034333/20240618_034333.log +aAcc : 96.8 +mIoU : 52.86 +mAcc : 57.8 +mDice : 57.81 +mFscore : 87.65 +mPrecision : 90.69 +mRecall : 57.8 +map25 : 50.88 +map50 : 48.51 +map75 : 40.12 +data_time : 0.0056 +time : 10.5532 +######################################## +project : fcn_hr48_1xb2-1k_hots-v1-512x512 +file_path : fcn_hr48_1xb2-1k_hots-v1-512x512/iter_500.pth/20240618_014739/20240618_014739.log +aAcc : 96.47 +mIoU : 51.22 +mAcc : 58.99 +mDice : 60.04 +mFscore : 67.18 +mPrecision : 79.2 +mRecall : 58.99 +map25 : 46.69 +map50 : 42.88 +map75 : 27.54 +data_time : 0.0056 +time : 10.6161 +######################################## +project : fpn_poolformer_s12_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : fpn_poolformer_s12_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_033708/20240618_033708.log +aAcc : 96.17 +mIoU : 49.91 +mAcc : 55.58 +mDice : 56.29 +mFscore : 82.68 +mPrecision : 88.88 +mRecall : 55.58 +map25 : 49.28 +map50 : 43.66 +map75 : 35.71 +data_time : 0.0056 +time : 10.5445 +######################################## +project : segmenter_vit-b_mask_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : segmenter_vit-b_mask_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_082333/20240618_082333.log +aAcc : 96.15 +mIoU : 41.94 +mAcc : 48.84 +mDice : 49.96 +mFscore : 67.08 +mPrecision : 75.55 +mRecall : 48.84 +map25 : 39.79 +map50 : 33.25 +map75 : 23.2 +data_time : 0.0057 +time : 10.6755 +############################################################ + +METRIC: mIoU +############################################################ +######################################## +project : mask2former_r101_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : mask2former_r101_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_054932/20240618_054932.log +aAcc : 97.61 +mIoU : 66.9 +mAcc : 76.76 +mDice : 76.35 +mFscore : 78.01 +mPrecision : 81.7 +mRecall : 76.76 +map25 : 56.06 +map50 : 51.5 +map75 : 42.51 +data_time : 0.0058 +time : 10.6307 +######################################## +project : fpn_poolformer_m48_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : fpn_poolformer_m48_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_033038/20240618_033038.log +aAcc : 97.73 +mIoU : 64.68 +mAcc : 68.45 +mDice : 69.82 +mFscore : 88.69 +mPrecision : 94.32 +mRecall : 68.45 +map25 : 57.4 +map50 : 56.2 +map75 : 49.43 +data_time : 0.0061 +time : 10.6644 +######################################## +project : mask2former_r50_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : mask2former_r50_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_055912/20240618_055912.log +aAcc : 97.26 +mIoU : 64.46 +mAcc : 75.81 +mDice : 75.04 +mFscore : 76.67 +mPrecision : 82.08 +mRecall : 75.81 +map25 : 58.36 +map50 : 51.44 +map75 : 42.62 +data_time : 0.0062 +time : 10.6537 +######################################## +project : mask2former_swin-t_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : mask2former_swin-t_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_060853/20240618_060853.log +aAcc : 97.31 +mIoU : 64.22 +mAcc : 74.04 +mDice : 74.42 +mFscore : 76.04 +mPrecision : 81.88 +mRecall : 74.04 +map25 : 53.13 +map50 : 48.36 +map75 : 41.15 +data_time : 0.0058 +time : 10.6339 +######################################## +project : knet-s3_r50-d8_fcn_1xb2-1k_hots-v1-512x512 +file_path : knet-s3_r50-d8_fcn_1xb2-1k_hots-v1-512x512/iter_500.pth/20240618_025148/20240618_025148.log +aAcc : 95.27 +mIoU : 58.53 +mAcc : 70.21 +mDice : 70.85 +mFscore : 70.85 +mPrecision : 78.46 +mRecall : 70.21 +map25 : 51.96 +map50 : 44.11 +map75 : 26.54 +data_time : 0.0055 +time : 10.6892 +######################################## +project : fpn_poolformer_m36_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : fpn_poolformer_m36_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_032407/20240618_032407.log +aAcc : 97.4 +mIoU : 56.15 +mAcc : 61.68 +mDice : 61.54 +mFscore : 85.07 +mPrecision : 89.88 +mRecall : 61.68 +map25 : 52.84 +map50 : 48.67 +map75 : 42.21 +data_time : 0.0056 +time : 10.6328 +######################################## +project : knet-s3_r50-d8_upernet_1xb2-1k_hots-v1-512x512 +file_path : knet-s3_r50-d8_upernet_1xb2-1k_hots-v1-512x512/iter_500.pth/20240618_053320/20240618_053320.log +aAcc : 95.56 +mIoU : 55.86 +mAcc : 64.83 +mDice : 66.72 +mFscore : 72.93 +mPrecision : 81.51 +mRecall : 64.83 +map25 : 52.35 +map50 : 42.71 +map75 : 26.45 +data_time : 0.0057 +time : 10.5901 +######################################## +project : knet-s3_r50-d8_fcn_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : knet-s3_r50-d8_fcn_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_051350/20240618_051350.log +aAcc : 95.86 +mIoU : 55.28 +mAcc : 67.78 +mDice : 65.14 +mFscore : 71.2 +mPrecision : 71.24 +mRecall : 67.78 +map25 : 18.16 +map50 : 15.94 +map75 : 10.94 +data_time : 0.007 +time : 10.6521 +######################################## +project : fpn_poolformer_s36_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : fpn_poolformer_s36_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_035000/20240618_035000.log +aAcc : 96.81 +mIoU : 54.71 +mAcc : 60.79 +mDice : 61.22 +mFscore : 82.21 +mPrecision : 89.3 +mRecall : 60.79 +map25 : 51.47 +map50 : 47.6 +map75 : 37.87 +data_time : 0.0057 +time : 10.6299 +######################################## +project : fpn_poolformer_s24_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : fpn_poolformer_s24_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_034333/20240618_034333.log +aAcc : 96.8 +mIoU : 52.86 +mAcc : 57.8 +mDice : 57.81 +mFscore : 87.65 +mPrecision : 90.69 +mRecall : 57.8 +map25 : 50.88 +map50 : 48.51 +map75 : 40.12 +data_time : 0.0056 +time : 10.5532 +############################################################ + +METRIC: mAcc +############################################################ +######################################## +project : mask2former_r101_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : 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mask2former_swin-t_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_060853/20240618_060853.log +aAcc : 97.31 +mIoU : 64.22 +mAcc : 74.04 +mDice : 74.42 +mFscore : 76.04 +mPrecision : 81.88 +mRecall : 74.04 +map25 : 53.13 +map50 : 48.36 +map75 : 41.15 +data_time : 0.0058 +time : 10.6339 +######################################## +project : fpn_poolformer_s24_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : fpn_poolformer_s24_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_034333/20240618_034333.log +aAcc : 96.8 +mIoU : 52.86 +mAcc : 57.8 +mDice : 57.81 +mFscore : 87.65 +mPrecision : 90.69 +mRecall : 57.8 +map25 : 50.88 +map50 : 48.51 +map75 : 40.12 +data_time : 0.0056 +time : 10.5532 +######################################## +project : fpn_poolformer_s36_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : fpn_poolformer_s36_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_035000/20240618_035000.log +aAcc : 96.81 +mIoU : 54.71 +mAcc : 60.79 +mDice : 61.22 +mFscore : 82.21 +mPrecision : 89.3 +mRecall : 60.79 +map25 : 51.47 +map50 : 47.6 +map75 : 37.87 +data_time : 0.0057 +time : 10.6299 +######################################## +project : fpn_poolformer_s12_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : fpn_poolformer_s12_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_033708/20240618_033708.log +aAcc : 96.17 +mIoU : 49.91 +mAcc : 55.58 +mDice : 56.29 +mFscore : 82.68 +mPrecision : 88.88 +mRecall : 55.58 +map25 : 49.28 +map50 : 43.66 +map75 : 35.71 +data_time : 0.0056 +time : 10.5445 +######################################## +project : fcn_hr48_1xb2-1k_hots-v1-512x512 +file_path : fcn_hr48_1xb2-1k_hots-v1-512x512/iter_500.pth/20240618_014739/20240618_014739.log +aAcc : 96.47 +mIoU : 51.22 +mAcc : 58.99 +mDice : 60.04 +mFscore : 67.18 +mPrecision : 79.2 +mRecall : 58.99 +map25 : 46.69 +map50 : 42.88 +map75 : 27.54 +data_time : 0.0056 +time : 10.6161 +######################################## +project : knet-s3_r50-d8_fcn_1xb2-1k_hots-v1-512x512 +file_path : knet-s3_r50-d8_fcn_1xb2-1k_hots-v1-512x512/iter_500.pth/20240618_025148/20240618_025148.log +aAcc : 95.27 +mIoU : 58.53 +mAcc : 70.21 +mDice : 70.85 +mFscore : 70.85 +mPrecision : 78.46 +mRecall : 70.21 +map25 : 51.96 +map50 : 44.11 +map75 : 26.54 +data_time : 0.0055 +time : 10.6892 +############################################################ + +METRIC: data_time +############################################################ +######################################## +project : fcn_hr18_1xb2-1k_hots-v1-512x512 +file_path : fcn_hr18_1xb2-1k_hots-v1-512x512/iter_500.pth/20240618_013452/20240618_013452.log +aAcc : 93.44 +mIoU : 26.1 +mAcc : 34.39 +mDice : 31.96 +mFscore : 62.58 +mPrecision : 66.89 +mRecall : 34.39 +map25 : 28.34 +map50 : 19.29 +map75 : 11.46 +data_time : 0.0053 +time : 10.5416 +######################################## +project : fpn_r101_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : fpn_r101_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_035626/20240618_035626.log +aAcc : 88.66 +mIoU : 10.4 +mAcc : 16.1 +mDice : 13.36 +mFscore : 44.84 +mPrecision : 49.12 +mRecall : 16.1 +map25 : 13.15 +map50 : 6.35 +map75 : 4.46 +data_time : 0.0054 +time : 10.636 +######################################## +project : icnet_r50-d8-in1k-pre_1xb2-1k_hots-v1-512x512 +file_path : icnet_r50-d8-in1k-pre_1xb2-1k_hots-v1-512x512/iter_500.pth/20240618_044748/20240618_044748.log +aAcc : 89.16 +mIoU : 10.14 +mAcc : 15.67 +mDice : 12.93 +mFscore : 55.25 +mPrecision : 55.2 +mRecall : 15.67 +map25 : 12.05 +map50 : 8.33 +map75 : 5.17 +data_time : 0.0054 +time : 10.563 +######################################## +project : maskformer_r50-d32_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : maskformer_r50-d32_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_025816/20240618_025816.log +aAcc : 91.07 +mIoU : 21.17 +mAcc : 29.61 +mDice : 29.28 +mFscore : 43.01 +mPrecision : 55.37 +mRecall : 29.61 +map25 : 17.8 +map50 : 12.13 +map75 : 6.86 +data_time : 0.0054 +time : 10.5751 +######################################## +project : fpn_r50_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : fpn_r50_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_040250/20240618_040250.log +aAcc : 85.19 +mIoU : 4.12 +mAcc : 7.82 +mDice : 5.52 +mFscore : 28.83 +mPrecision : 31.01 +mRecall : 7.82 +map25 : 4.46 +map50 : 3.42 +map75 : 3.32 +data_time : 0.0055 +time : 10.5888 +######################################## +project : icnet_r18-d8-in1k-pre_1xb2-1k_hots-v1-512x512 +file_path : icnet_r18-d8-in1k-pre_1xb2-1k_hots-v1-512x512/iter_500.pth/20240618_043500/20240618_043500.log +aAcc : 88.72 +mIoU : 9.33 +mAcc : 14.43 +mDice : 11.75 +mFscore : 61.35 +mPrecision : 51.1 +mRecall : 14.43 +map25 : 11.79 +map50 : 6.87 +map75 : 3.51 +data_time : 0.0055 +time : 10.5626 +######################################## +project : nonlocal_r50-d8_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : nonlocal_r50-d8_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_070631/20240618_070631.log +aAcc : 92.31 +mIoU : 18.05 +mAcc : 25.31 +mDice : 22.06 +mFscore : 54.57 +mPrecision : 62.18 +mRecall : 25.31 +map25 : 20.15 +map50 : 14.38 +map75 : 9.51 +data_time : 0.0055 +time : 10.5964 +######################################## +project : knet-s3_r50-d8_fcn_1xb2-1k_hots-v1-512x512 +file_path : knet-s3_r50-d8_fcn_1xb2-1k_hots-v1-512x512/iter_500.pth/20240618_025148/20240618_025148.log +aAcc : 95.27 +mIoU : 58.53 +mAcc : 70.21 +mDice : 70.85 +mFscore : 70.85 +mPrecision : 78.46 +mRecall : 70.21 +map25 : 51.96 +map50 : 44.11 +map75 : 26.54 +data_time : 0.0055 +time : 10.6892 +######################################## +project : pointrend_r50_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : pointrend_r50_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_072544/20240618_072544.log +aAcc : 78.67 +mIoU : 4.3 +mAcc : 12.35 +mDice : 6.14 +mFscore : 13.74 +mPrecision : 11.32 +mRecall : 12.35 +map25 : 3.95 +map50 : 2.36 +map75 : 2.13 +data_time : 0.0055 +time : 10.5993 +######################################## +project : segformer_mit-b0_1xb2-pre-ade20k-1k_hots-v1-512x512 +file_path : segformer_mit-b0_1xb2-pre-ade20k-1k_hots-v1-512x512/iter_500.pth/20240618_080418/20240618_080418.log +aAcc : 90.3 +mIoU : 14.55 +mAcc : 21.04 +mDice : 19.33 +mFscore : 47.82 +mPrecision : 60.9 +mRecall : 21.04 +map25 : 19.31 +map50 : 9.7 +map75 : 5.68 +data_time : 0.0055 +time : 10.5478 +segmenter_vit-b_mask_1xb2-pre-ade20k-1k_hots-v1-512x512 : 1 +segnext_mscan-b_1xb2-pre-ade20k-1k_hots-v1-512x512 : 1 +fcn_hr18_1xb2-1k_hots-v1-512x512 : 1 +fpn_r101_1xb2-pre-ade20k-1k_hots-v1-512x512 : 1 +icnet_r50-d8-in1k-pre_1xb2-1k_hots-v1-512x512 : 1 +maskformer_r50-d32_1xb2-pre-ade20k-1k_hots-v1-512x512 : 1 +fpn_r50_1xb2-pre-ade20k-1k_hots-v1-512x512 : 1 +icnet_r18-d8-in1k-pre_1xb2-1k_hots-v1-512x512 : 1 +nonlocal_r50-d8_1xb2-pre-ade20k-1k_hots-v1-512x512 : 1 +pointrend_r50_1xb2-pre-ade20k-1k_hots-v1-512x512 : 1 +segformer_mit-b0_1xb2-pre-ade20k-1k_hots-v1-512x512 : 1 +knet-s3_r50-d8_deeplabv3_1xb2-pre-ade20k-1k_hots-v1-512x512 : 2 +twins_svt-b_fpn_fpnhead_1xb2-pre-ade20k-1k_hots-v1-512x512 : 2 +fcn_hr48_1xb2-1k_hots-v1-512x512 : 3 +knet-s3_r50-d8_fcn_1xb2-pre-ade20k-1k_hots-v1-512x512 : 4 +knet-s3_r50-d8_pspnet_1xb2-1k_hots-v1-512x512 : 4 +fpn_poolformer_s12_1xb2-pre-ade20k-1k_hots-v1-512x512 : 5 +knet-s3_r50-d8_upernet_1xb2-1k_hots-v1-512x512 : 6 +fpn_poolformer_s24_1xb2-pre-ade20k-1k_hots-v1-512x512 : 7 +fpn_poolformer_s36_1xb2-pre-ade20k-1k_hots-v1-512x512 : 8 +knet-s3_r50-d8_fcn_1xb2-1k_hots-v1-512x512 : 8 +fpn_poolformer_m48_1xb2-pre-ade20k-1k_hots-v1-512x512 : 10 +mask2former_r101_1xb2-pre-ade20k-1k_hots-v1-512x512 : 10 +fpn_poolformer_m36_1xb2-pre-ade20k-1k_hots-v1-512x512 : 10 +mask2former_swin-t_1xb2-pre-ade20k-1k_hots-v1-512x512 : 10 +mask2former_r50_1xb2-pre-ade20k-1k_hots-v1-512x512 : 10 diff --git a/my_projects/selection_data/selected_models_scores_mmlab b/my_projects/selection_data/selected_models_scores_mmlab new file mode 100644 index 0000000000..b9e5b4ac34 --- /dev/null +++ b/my_projects/selection_data/selected_models_scores_mmlab @@ -0,0 +1,2387 @@ +fastscnn +ann +apcnet +beit +bisenetv1 +bisenetv2 +ccnet +cgnet +convnext +danet +ddrnet +deeplabv3 +deeplabv3plus +dmnet +dnlnet +dpt +dsdl +emanet +encnet +erfnet +fastfcn +fcn +gcnet +hrnet +icnet +isanet +knet +mae +mask2former +maskformer +mobilenet_v2 +mobilenet_v3 +my_configs +my_configs copy +nonlocal_net +ocrnet +pidnet +point_rend +poolformer +psanet +pspnet +resnest +san +segformer +segmenter +segnext +sem_fpn +setr +stdc +swin +twins +unet +upernet +vit +vpd +517 +Dataset: COCO-Stuff 164k + +top 4 of custom_score: + +Rank: 1 +config : configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py +Mem (GB) : 6.33 +Inf time (fps) : 74.24 +mIoU : 28.55 +custom_score : 16.238546603475513 +fps_per_GB : 11.72827804107425 +mIoU_per_spf : 2119.552 + +Rank: 2 +config : configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py +Mem (GB) : 9.28 +Inf time (fps) : 32.6 +mIoU : 34.88 +custom_score : 7.271551724137932 +fps_per_GB : 3.512931034482759 +mIoU_per_spf : 1137.0880000000002 + +Rank: 3 +config : configs/pspnet/pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py +Mem (GB) : 9.6 +Inf time (fps) : 20.5 +mIoU : 40.53 +custom_score : 6.357291666666667 +fps_per_GB : 2.135416666666667 +mIoU_per_spf : 830.865 + +Rank: 4 +config : configs/deeplabv3/deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py +Mem (GB) : 9.6 +Inf time (fps) : 10.8 +mIoU : 41.37 +custom_score : 5.434375 +fps_per_GB : 1.1250000000000002 +mIoU_per_spf : 446.796 + + +top 4 of fps_per_GB: + +Rank: 1 +config : configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py +Mem (GB) : 6.33 +Inf time (fps) : 74.24 +mIoU : 28.55 +custom_score : 16.238546603475513 +fps_per_GB : 11.72827804107425 +mIoU_per_spf : 2119.552 + +Rank: 2 +config : configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py +Mem (GB) : 9.28 +Inf time (fps) : 32.6 +mIoU : 34.88 +custom_score : 7.271551724137932 +fps_per_GB : 3.512931034482759 +mIoU_per_spf : 1137.0880000000002 + +Rank: 3 +config : configs/pspnet/pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py +Mem (GB) : 9.6 +Inf time (fps) : 20.5 +mIoU : 40.53 +custom_score : 6.357291666666667 +fps_per_GB : 2.135416666666667 +mIoU_per_spf : 830.865 + +Rank: 4 +config : configs/deeplabv3/deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py +Mem (GB) : 9.6 +Inf time (fps) : 10.8 +mIoU : 41.37 +custom_score : 5.434375 +fps_per_GB : 1.1250000000000002 +mIoU_per_spf : 446.796 + + +top 4 of mIoU_per_spf: + +Rank: 1 +config : configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py +Mem (GB) : 6.33 +Inf time (fps) : 74.24 +mIoU : 28.55 +custom_score : 16.238546603475513 +fps_per_GB : 11.72827804107425 +mIoU_per_spf : 2119.552 + +Rank: 2 +config : configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py +Mem (GB) : 9.28 +Inf time (fps) : 32.6 +mIoU : 34.88 +custom_score : 7.271551724137932 +fps_per_GB : 3.512931034482759 +mIoU_per_spf : 1137.0880000000002 + +Rank: 3 +config : configs/pspnet/pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py +Mem (GB) : 9.6 +Inf time (fps) : 20.5 +mIoU : 40.53 +custom_score : 6.357291666666667 +fps_per_GB : 2.135416666666667 +mIoU_per_spf : 830.865 + +Rank: 4 +config : configs/deeplabv3/deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py +Mem (GB) : 9.6 +Inf time (fps) : 10.8 +mIoU : 41.37 +custom_score : 5.434375 +fps_per_GB : 1.1250000000000002 +mIoU_per_spf : 446.796 + + +top 4 of Inf time (fps): + +Rank: 1 +config : configs/bisenetv1/bisenetv1_r18-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py +Mem (GB) : 6.33 +Inf time (fps) : 74.24 +mIoU : 28.55 +custom_score : 16.238546603475513 +fps_per_GB : 11.72827804107425 +mIoU_per_spf : 2119.552 + +Rank: 2 +config : configs/bisenetv1/bisenetv1_r50-d32-in1k-pre_4xb4-160k_coco-stuff164k-512x512.py +Mem (GB) : 9.28 +Inf time (fps) : 32.6 +mIoU : 34.88 +custom_score : 7.271551724137932 +fps_per_GB : 3.512931034482759 +mIoU_per_spf : 1137.0880000000002 + +Rank: 3 +config : configs/pspnet/pspnet_r50-d8_4xb4-320k_coco-stuff164k-512x512.py +Mem (GB) : 9.6 +Inf time (fps) : 20.5 +mIoU : 40.53 +custom_score : 6.357291666666667 +fps_per_GB : 2.135416666666667 +mIoU_per_spf : 830.865 + +Rank: 4 +config : configs/deeplabv3/deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py +Mem (GB) : 9.6 +Inf time (fps) : 10.8 +mIoU : 41.37 +custom_score : 5.434375 +fps_per_GB : 1.1250000000000002 +mIoU_per_spf : 446.796 + + +top 4 of mIoU: + +Rank: 1 +config : configs/deeplabv3/deeplabv3_r50-d8_4xb4-320k_coco-stuff164k-512x512.py +Mem (GB) : 9.6 +Inf time (fps) : 10.8 +mIoU : 41.37 +custom_score : 5.434375 +fps_per_GB : 1.1250000000000002 +mIoU_per_spf : 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+mIoU(ms+flip) : 37.24 +config : configs/pspnet/pspnet_r50-d8_4xb4-40k_coco-stuff10k-512x512.py +dataset : COCO-Stuff 10k +custom_score : 5.919791666666667 +fps_per_GB : 2.135416666666667 +mIoU_per_spf : 744.765 + +Method : DeepLabV3 +Backbone : R-50-D8 +Crop Size : 512x512 +Lr schd : 40000 +Mem (GB) : 9.6 +Inf time (fps) : 10.8 +Device : V100 +mIoU : 35.73 +mIoU(ms+flip) : 37.09 +config : configs/deeplabv3/deeplabv3_r50-d8_4xb4-40k_coco-stuff10k-512x512.py +dataset : COCO-Stuff 10k +custom_score : 4.846875000000001 +fps_per_GB : 1.1250000000000002 +mIoU_per_spf : 385.884 + +Method : Segmenter Mask +Backbone : ViT-T_16 +Crop Size : 512x512 +Lr schd : 160000 +Mem (GB) : 1.21 +Inf time (fps) : 27.98 +Device : V100 +mIoU : 39.99 +mIoU(ms+flip) : 40.83 +config : configs/segmenter/segmenter_vit-t_mask_8xb1-160k_ade20k-512x512.py +dataset : ADE20K +custom_score : 56.17355371900827 +fps_per_GB : 23.123966942148762 +mIoU_per_spf : 1118.9202 + +Method : MaskFormer +Backbone : R-50-D32 +Crop Size : 512x512 +Lr schd : 160000 +Mem (GB) : 3.29 +Inf time (fps) : 100.0 +Device : 42.20 +mIoU : 44.29 +mIoU(ms+flip) : - +config : configs/maskformer/maskformer_r50-d32_8xb2-160k_ade20k-512x512.py +dataset : ADE20K +custom_score : 43.857142857142854 +fps_per_GB : 30.3951367781155 +mIoU_per_spf : 4429.0 + +Method : Segformer +Backbone : MIT-B0 +Crop Size : 512x512 +Lr schd : 160000 +Mem (GB) : 2.1 +Inf time (fps) : 51.32 +Device : 1080 Ti +mIoU : 37.41 +mIoU(ms+flip) : 38.34 +config : configs/segformer/segformer_mit-b0_8xb2-160k_ade20k-512x512.py +dataset : ADE20K +custom_score : 42.252380952380946 +fps_per_GB : 24.438095238095237 +mIoU_per_spf : 1919.8811999999998 + +Method : Segmenter Linear +Backbone : ViT-S_16 +Crop Size : 512x512 +Lr schd : 160000 +Mem (GB) : 1.78 +Inf time (fps) : 28.07 +Device : V100 +mIoU : 45.75 +mIoU(ms+flip) : 46.82 +config : configs/segmenter/segmenter_vit-s_fcn_8xb1-160k_ade20k-512x512.py +dataset : ADE20K +custom_score : 41.471910112359545 +fps_per_GB : 15.769662921348315 +mIoU_per_spf : 1284.2024999999999 + +Method : MaskFormer +Backbone : Swin-T +Crop Size : 512x512 +Lr schd : 160000 +Mem (GB) : 3.73 +Inf time (fps) : 100.0 +Device : 40.53 +mIoU : 46.69 +mIoU(ms+flip) : - +config : configs/maskformer/maskformer_swin-t_upernet_8xb2-160k_ade20k-512x512.py +dataset : ADE20K +custom_score : 39.32707774798928 +fps_per_GB : 26.80965147453083 +mIoU_per_spf : 4669.0 + +Method : MaskFormer +Backbone : R-101-D32 +Crop Size : 512x512 +Lr schd : 160000 +Mem (GB) : 4.12 +Inf time (fps) : 100.0 +Device : 34.90 +mIoU : 45.11 +mIoU(ms+flip) : - +config : configs/maskformer/maskformer_r101-d32_8xb2-160k_ade20k-512x512.py +dataset : ADE20K +custom_score : 35.22087378640777 +fps_per_GB : 24.271844660194173 +mIoU_per_spf : 4511.0 + +Method : SegNeXt +Backbone : MSCAN-T +Crop Size : 512x512 +Lr schd : 160000 +Mem (GB) : 17.88 +Inf time (fps) : 52.38 +Device : A100 +mIoU : 41.5 +mIoU(ms+flip) : 42.59 +config : configs/segnext/segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py +dataset : ADE20K +custom_score : 5.250559284116331 +fps_per_GB : 2.929530201342282 +mIoU_per_spf : 2173.77 + +Method : FPN +Backbone : R-50 +Crop Size : 512x512 +Lr schd : 160000 +Mem (GB) : 4.9 +Inf time (fps) : 55.77 +Device : V100 +mIoU : 37.49 +mIoU(ms+flip) : 39.09 +config : configs/sem_fpn/fpn_r50_4xb4-160k_ade20k-512x512.py +dataset : ADE20K +custom_score : 19.03265306122449 +fps_per_GB : 11.381632653061224 +mIoU_per_spf : 2090.8173000000006 + +Method : FCN +Backbone : M-V2-D8 +Crop Size : 512x512 +Lr schd : 160000 +Mem (GB) : 6.5 +Inf time (fps) : 64.4 +Device : V100 +mIoU : 19.71 +mIoU(ms+flip) : - +config : configs/mobilenet_v2/mobilenet-v2-d8_fcn_4xb4-160k_ade20k-512x512.py +dataset : ADE20K +custom_score : 12.940000000000001 +fps_per_GB : 9.907692307692308 +mIoU_per_spf : 1269.3240000000003 + +Method : PSPNet +Backbone : M-V2-D8 +Crop Size : 512x512 +Lr schd : 160000 +Mem (GB) : 6.5 +Inf time (fps) : 57.7 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configs/ocrnet/ocrnet_hr18s_4xb4-40k_voc12aug-512x512.py +dataset : Pascal VOC 2012 + Aug +custom_score : 29.802857142857142 +fps_per_GB : 9.014285714285714 +mIoU_per_spf : 2295.578 + +Method : OCRNet +Backbone : HRNetV2p-W18 +Crop Size : 512x512 +Lr schd : 40000 +Mem (GB) : 4.7 +Inf time (fps) : 19.91 +Device : V100 +mIoU : 74.98 +mIoU(ms+flip) : 77.40 +config : configs/ocrnet/ocrnet_hr18_4xb4-40k_voc12aug-512x512.py +dataset : Pascal VOC 2012 + Aug +custom_score : 20.18936170212766 +fps_per_GB : 4.236170212765957 +mIoU_per_spf : 1492.8518000000001 + +Method : GCNet +Backbone : R-50-D8 +Crop Size : 512x512 +Lr schd : 20000 +Mem (GB) : 5.8 +Inf time (fps) : 23.35 +Device : V100 +mIoU : 76.42 +mIoU(ms+flip) : 77.51 +config : configs/gcnet/gcnet_r50-d8_4xb4-20k_voc12aug-512x512.py +dataset : Pascal VOC 2012 + Aug +custom_score : 17.201724137931038 +fps_per_GB : 4.025862068965518 +mIoU_per_spf : 1784.4070000000002 + +Method : FCN +Backbone : R-50-D8 +Crop Size : 512x512 +Lr schd : 20000 +Mem (GB) : 5.7 +Inf time (fps) : 23.28 +Device : V100 +mIoU : 67.08 +mIoU(ms+flip) : 69.94 +config : configs/fcn/fcn_r50-d8_4xb4-20k_voc12aug-512x512.py +dataset : Pascal VOC 2012 + Aug +custom_score : 15.852631578947367 +fps_per_GB : 4.08421052631579 +mIoU_per_spf : 1561.6224 + +Method : PSPNet +Backbone : R-50-D8 +Crop Size : 512x512 +Lr schd : 40000 +Mem (GB) : 6.1 +Inf time (fps) : 23.59 +Device : V100 +mIoU : 77.29 +mIoU(ms+flip) : 78.48 +config : configs/pspnet/pspnet_r50-d8_4xb4-40k_voc12aug-512x512.py +dataset : Pascal VOC 2012 + Aug +custom_score : 16.53770491803279 +fps_per_GB : 3.8672131147540987 +mIoU_per_spf : 1823.2711000000002 + +Method : ISANet +Backbone : R-50-D8 +Crop Size : 512x512 +Lr schd : 20000 +Mem (GB) : 5.9 +Inf time (fps) : 23.08 +Device : V100 +mIoU : 76.78 +mIoU(ms+flip) : 77.79 +config : configs/isanet/isanet_r50-d8_4xb4-20k_voc12aug-512x512.py +dataset : Pascal VOC 2012 + Aug +custom_score : 16.92542372881356 +fps_per_GB : 3.9118644067796606 +mIoU_per_spf : 1772.0824 + +Method : DeepLabV3 +Backbone : R-101-D8 +Crop Size : 512x512 +Lr schd : 20000 +Mem (GB) : 9.6 +Inf time (fps) : 9.81 +Device : V100 +mIoU : 78.7 +mIoU(ms+flip) : 79.95 +config : configs/deeplabv3/deeplabv3_r101-d8_4xb4-20k_voc12aug-512x512.py +dataset : Pascal VOC 2012 + Aug +custom_score : 9.219791666666667 +fps_per_GB : 1.021875 +mIoU_per_spf : 772.047 + +Method : PSPNet +Backbone : R-101-D8 +Crop Size : 512x512 +Lr schd : 40000 +Mem (GB) : 9.6 +Inf time (fps) : 15.02 +Device : V100 +mIoU : 78.52 +mIoU(ms+flip) : 79.57 +config : configs/pspnet/pspnet_r101-d8_4xb4-40k_voc12aug-512x512.py +dataset : Pascal VOC 2012 + Aug +custom_score : 9.74375 +fps_per_GB : 1.5645833333333334 +mIoU_per_spf : 1179.3703999999998 + +Method : ISANet +Backbone : R-101-D8 +Crop Size : 512x512 +Lr schd : 20000 +Mem (GB) : 9.465 +Inf time (fps) : 7.42 +Device : V100 +mIoU : 78.46 +mIoU(ms+flip) : 79.16 +config : configs/isanet/isanet_r101-d8_4xb4-20k_voc12aug-512x512.py +dataset : Pascal VOC 2012 + Aug +custom_score : 9.073428420496565 +fps_per_GB : 0.7839408346539883 +mIoU_per_spf : 582.1732 + +Method : NonLocalNet +Backbone : R-101-D8 +Crop Size : 512x512 +Lr schd : 40000 +Mem (GB) : 9.8 +Inf time (fps) : 14.01 +Device : V100 +mIoU : 78.27 +mIoU(ms+flip) : 79.12 +config : configs/nonlocal_net/nonlocal_r101-d8_4xb4-40k_voc12aug-512x512.py +dataset : Pascal VOC 2012 + Aug +custom_score : 9.416326530612244 +fps_per_GB : 1.4295918367346938 +mIoU_per_spf : 1096.5627 + +Method : CCNet +Backbone : R-101-D8 +Crop Size : 512x512 +Lr schd : 40000 +Mem (GB) : 9.5 +Inf time (fps) : 13.64 +Device : V100 +mIoU : 77.87 +mIoU(ms+flip) : 78.90 +config : configs/ccnet/ccnet_r101-d8_4xb4-40k_voc12aug-512x512.py +dataset : Pascal VOC 2012 + Aug +custom_score : 9.632631578947368 +fps_per_GB : 1.4357894736842105 +mIoU_per_spf : 1062.1468000000002 + +Method : FCN +Backbone : HRNetV2p-W48 +Crop Size : 480x480 +Lr schd : 80000 +Mem (GB) : 6.1 +Inf time (fps) : 8.86 +Device : V100 +mIoU : 51.12 +mIoU(ms+flip) : 53.56 +config : configs/hrnet/fcn_hr48_4xb4-80k_pascal-context-59-480x480.py +dataset : Pascal Context 59 +custom_score : 9.832786885245902 +fps_per_GB : 1.4524590163934425 +mIoU_per_spf : 452.9231999999999 + +Method : PSPNet +Backbone : R-101-D8 +Crop Size : 480x480 +Lr schd : 80000 +Mem (GB) : 8.8 +Inf time (fps) : 9.68 +Device : V100 +mIoU : 52.47 +mIoU(ms+flip) : 53.99 +config : configs/pspnet/pspnet_r101-d8_4xb4-80k_pascal-context-59-480x480.py +dataset : Pascal Context 59 +custom_score : 7.062499999999999 +fps_per_GB : 1.0999999999999999 +mIoU_per_spf : 507.90959999999995 + +Method : DeepLabV3 +Backbone : R-101-D8 +Crop Size : 480x480 +Lr schd : 40000 +Mem (GB) : 9.2 +Inf time (fps) : 7.09 +Device : V100 +mIoU : 52.61 +mIoU(ms+flip) : 54.28 +config : configs/deeplabv3/deeplabv3_r101-d8_4xb4-40k_pascal-context-59-480x480.py +dataset : Pascal Context 59 +custom_score : 6.489130434782609 +fps_per_GB : 0.7706521739130435 +mIoU_per_spf : 373.00489999999996 + +Method : DDRNet +Backbone : DDRNet23-slim +Crop Size : 1024x1024 +Lr schd : 120000 +Mem (GB) : 1.7 +Inf time (fps) : 85.85 +Device : A100 +mIoU : 77.84 +mIoU(ms+flip) : 80.15 +config : configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-120k_cityscapes-1024x1024.py +dataset : Cityscapes +custom_score : 96.28823529411765 +fps_per_GB : 50.5 +mIoU_per_spf : 6682.563999999999 + +Method : PSPNet +Backbone : R-18b-D8 +Crop Size : 512x1024 +Lr schd : 80000 +Mem (GB) : 1.5 +Inf time (fps) : 16.28 +Device : V100 +mIoU : 74.23 +mIoU(ms+flip) : 75.79 +config : configs/pspnet/pspnet_r18b-d8_4xb2-80k_cityscapes-512x1024.py +dataset : Cityscapes +custom_score : 60.34 +fps_per_GB : 10.853333333333333 +mIoU_per_spf : 1208.4644 + +Method : FCN +Backbone : HRNetV2p-W18-Small +Crop Size : 512x1024 +Lr schd : 160000 +Mem (GB) : 1.7 +Inf time (fps) : 23.74 +Device : V100 +mIoU : 76.31 +mIoU(ms+flip) : 78.31 +config : configs/hrnet/fcn_hr18s_4xb2-160k_cityscapes-512x1024.py +dataset : Cityscapes +custom_score : 58.85294117647059 +fps_per_GB : 13.964705882352941 +mIoU_per_spf : 1811.5994 + +Method : ICNet +Backbone : R-18-D8 +Crop Size : 832x832 +Lr schd : 160000 +Mem (GB) : 1.7 +Inf time (fps) : 27.12 +Device : V100 +mIoU : 71.64 +mIoU(ms+flip) : 74.18 +config : configs/icnet/icnet_r18-d8_4xb2-160k_cityscapes-832x832.py +dataset : Cityscapes +custom_score : 58.09411764705883 +fps_per_GB : 15.952941176470588 +mIoU_per_spf : 1942.8768 + +Method : DeepLabV3 +Backbone : R-18b-D8 +Crop Size : 512x1024 +Lr schd : 80000 +Mem (GB) : 1.6 +Inf time (fps) : 13.93 +Device : V100 +mIoU : 76.26 +mIoU(ms+flip) : 77.88 +config : configs/deeplabv3/deeplabv3_r18b-d8_4xb2-80k_cityscapes-512x1024.py +dataset : Cityscapes +custom_score : 56.36875 +fps_per_GB : 8.706249999999999 +mIoU_per_spf : 1062.3018000000002 + +Method : PIDNet +Backbone : PIDNet-S +Crop Size : 1024x1024 +Lr schd : 120000 +Mem (GB) : 3.38 +Inf time (fps) : 80.82 +Device : A100 +mIoU : 78.74 +mIoU(ms+flip) : 80.87 +config : configs/pidnet/pidnet-s_2xb6-120k_1024x1024-cityscapes.py +dataset : Cityscapes +custom_score : 47.20710059171598 +fps_per_GB : 23.911242603550296 +mIoU_per_spf : 6363.766799999999 + +Method : FastSCNN +Backbone : FastSCNN +Crop Size : 512x1024 +Lr schd : 160000 +Mem (GB) : 3.3 +Inf time (fps) : 56.45 +Device : V100 +mIoU : 70.96 +mIoU(ms+flip) : 72.65 +config : configs/fastscnn/fast_scnn_8xb4-160k_cityscapes-512x1024.py +dataset : Cityscapes +custom_score : 38.60909090909091 +fps_per_GB : 17.10606060606061 +mIoU_per_spf : 4005.692 + +Method : PIDNet +Backbone : PIDNet-M +Crop Size : 1024x1024 +Lr schd : 120000 +Mem (GB) : 5.14 +Inf time (fps) : 71.98 +Device : A100 +mIoU : 80.22 +mIoU(ms+flip) : 82.05 +config : configs/pidnet/pidnet-m_2xb6-120k_1024x1024-cityscapes.py +dataset : Cityscapes +custom_score : 29.610894941634243 +fps_per_GB : 14.00389105058366 +mIoU_per_spf : 5774.2356 + +Method : PIDNet +Backbone : PIDNet-L +Crop Size : 1024x1024 +Lr schd : 120000 +Mem (GB) : 5.83 +Inf time (fps) : 60.06 +Device : A100 +mIoU : 80.89 +mIoU(ms+flip) : 82.37 +config : configs/pidnet/pidnet-l_2xb6-120k_1024x1024-cityscapes.py +dataset : Cityscapes +custom_score : 24.17667238421955 +fps_per_GB : 10.30188679245283 +mIoU_per_spf : 4858.2534000000005 + +Method : DDRNet +Backbone : DDRNet23 +Crop Size : 1024x1024 +Lr schd : 120000 +Mem (GB) : 7.26 +Inf time (fps) : 33.41 +Device : A100 +mIoU : 79.99 +mIoU(ms+flip) : 81.71 +config : configs/ddrnet/ddrnet_23_in1k-pre_2xb6-120k_cityscapes-1024x1024.py +dataset : Cityscapes +custom_score : 15.6198347107438 +fps_per_GB : 4.601928374655647 +mIoU_per_spf : 2672.4658999999992 + +Method : FCN +Backbone : HRNetV2p-W18 +Crop Size : 512x1024 +Lr schd : 160000 +Mem (GB) : 2.9 +Inf time (fps) : 12.97 +Device : V100 +mIoU : 78.8 +mIoU(ms+flip) : 80.74 +config : configs/hrnet/fcn_hr18_4xb2-160k_cityscapes-512x1024.py +dataset : Cityscapes +custom_score : 31.644827586206898 +fps_per_GB : 4.472413793103448 +mIoU_per_spf : 1022.0360000000001 + diff --git a/my_projects/selection_data/traverse.py b/my_projects/selection_data/traverse.py new file mode 100644 index 0000000000..abdffc756f --- /dev/null +++ b/my_projects/selection_data/traverse.py @@ -0,0 +1,121 @@ +import os + +dict_list = [] +for project_name in os.listdir("."): + if not os.path.isdir(project_name): + continue + if "iter_500.pth" not in os.listdir(project_name): + continue + proj_iter_pth = os.path.join(project_name, "iter_500.pth") + # in iter 500 + for exp_dir in os.listdir(proj_iter_pth): + exp_dir_path = os.path.join(proj_iter_pth, exp_dir) + if not os.path.isdir(exp_dir_path): + continue + for file_name in os.listdir(exp_dir_path): + if '.log' not in file_name: + continue + log_file_path = os.path.join(exp_dir_path, file_name) + with open(log_file_path, 'r') as log_file: + data = log_file.readlines()[-1] + data = data.split("]")[-1].split() + + data_dict = {} + data_dict["project"] = project_name + data_dict["file_path"] = log_file_path + for idx in range(0, len(data) - 1, 2): + data_dict[data[idx][:-1]] = data[idx + 1] + if data[idx + 1].isnumeric(): + data_dict[data[idx][:-1]] = data[idx + 1] + # for key, val in data_dict.items(): + # print(f"{key} : {val}") + # print('#' * 40) + dict_list.append(data_dict) + + +req_keys = dict_list[-1].keys() +rm_list = [] +print(f"required keys: {req_keys}") +for data_dict in dict_list: + if req_keys != data_dict.keys(): + + rm_list.append(data_dict) + # if 'mIoU' not in data_dict.keys(): + + # print(data_dict) + +print(f"remove_list:\n {rm_list}") +print() +dict_list = [d_dict for d_dict in dict_list if d_dict not in rm_list] + +for d_dict in dict_list: + for key, val in d_dict.items(): + if key not in ["file_path", "project"]: + d_dict[key] = float(val) +print(f"{'#' * 60}\n{'#' * 60}\n{'#' * 60}") + + +# ['aAcc:', '89.0800', 'mIoU:', '10.8400', 'mAcc:', '16.3700', 'mDice:', '13.6600', +# 'mFscore:', '45.8600', 'mPrecision:', '44.7000', 'mRecall:', '16.3700', 'map25:', +# '13.5800', 'map50:', '8.5500', 'map75:', '6.6700', 'data_time:', '0.0058', +# 'time:', '10.6031'] +from operator import itemgetter +from copy import deepcopy + +def get_top_n_metric(dict_list, n = 10, metric ='mIoU', reverse=True): + # sort_list = sorted(dict_list, key=itemgetter(metric), reverse=True) + sort_list = sorted(dict_list, key=lambda d : d[metric], reverse=reverse) + if len(sort_list) >= n: + return sort_list[:n] + return sort_list + +def print_dict_list(dict_list): + for data_dict in dict_list: + print('#' * 40) + for key, val in data_dict.items(): + print(f"{key} : {val}") + + +selection = ['aAcc', 'mIoU', 'mAcc', 'mDice', 'mFscore', + 'mPrecision', 'mRecall', 'map50', 'map25', 'map75', 'data_time' + + ] + +proj_hist = {} +for metric in selection: + print('#'* 60) + print(f"\nMETRIC: {metric}") + print('#'* 60) + reverse = True + if metric == 'data_time': + reverse = False + top_10 = get_top_n_metric( + dict_list=deepcopy(dict_list), n=10, metric=metric, reverse=reverse + ) + for item in top_10: + proj_name = item["project"] + if proj_name in proj_hist.keys(): + proj_hist[proj_name] += 1 + else: + proj_hist[proj_name] = 1 + + print_dict_list(top_10) + +proj_hist = {k: v for k, v in sorted(proj_hist.items(), key=lambda item : item[1])} +for key, val in proj_hist.items(): + print(f"{key} : {val}") +# print(len(dict_list)) +# print("type miou") +# print(type(dict_list[0]['mIoU'])) +# for metric in selection: +# high_score = 0 +# best_model = None +# for mdl_dict in dict_list: +# score = mdl_dict[metric] +# if score > high_score: +# best_model = mdl_dict +# high_score = score +# print(f"METRIC: {metric}\nBEST:") +# for key, val in best_model.items(): +# print(f"{key} : {val}") +# print('#'* 60) \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/20241107_134943/20241107_134943.json b/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/20241107_134943/20241107_134943.json new file mode 100644 index 0000000000..e49a9ab0f8 --- /dev/null +++ b/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/20241107_134943/20241107_134943.json @@ -0,0 +1 @@ +{"mPr@50": 56.6, "mPr@60": 49.49, "mPr@70": 41.35, "mPr@80": 29.18, "mPr@90": 11.78, "mIoU": 47.81, "data_time": 0.7758326884543542, "time": 0.8117871544148663} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/benchmark/fps_20241107_135117.json b/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/benchmark/fps_20241107_135117.json new file mode 100644 index 0000000000..c9434ea2a7 --- /dev/null +++ b/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/benchmark/fps_20241107_135117.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512.py", + "unit": "img / s", + "overall_fps_1": 99.1, + "overall_memory_1": 204, + "overall_fps_2": 94.36, + "overall_memory_2": 206, + "overall_fps_3": 94.83, + "overall_memory_3": 205, + "overall_fps_4": 94.39, + "overall_memory_4": 205, + "overall_fps_5": 99.23, + "overall_memory_5": 206, + "overall_fps_6": 96.14, + "overall_memory_6": 204, + "overall_fps_7": 98.17, + "overall_memory_7": 205, + "overall_fps_8": 98.83, + "overall_memory_8": 205, + "overall_fps_9": 100.25, + "overall_memory_9": 204, + "overall_fps_10": 100.23, + "overall_memory_10": 205, + "FPS": 97.55, + "Var FPS": 5.1289, + "Mem (MB)": 204.9, + "Var Mem": 0.49 +} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/bisenetv1_label_results.json b/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/bisenetv1_label_results.json new file mode 100644 index 0000000000..735acc026c --- /dev/null +++ b/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/bisenetv1_label_results.json @@ -0,0 +1,258 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 98.7 + }, + "apple": { + "Pr@50": 72.73, + "Pr@60": 63.64, + "Pr@70": 63.64, + "Pr@80": 36.36, + "Pr@90": 0.0, + "IoU": 64.23 + }, + "ball": { + "Pr@50": 100.0, + "Pr@60": 75.0, + "Pr@70": 68.75, + "Pr@80": 43.75, + "Pr@90": 0.0, + "IoU": 66.1 + }, + "banana": { + "Pr@50": 100.0, + "Pr@60": 89.47, + "Pr@70": 78.95, + "Pr@80": 68.42, + "Pr@90": 0.0, + "IoU": 79.43 + }, + "bell_pepper": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 34.11 + }, + "binder": { + "Pr@50": 61.11, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 33.33, + "Pr@90": 27.78, + "IoU": 70.96 + }, + "bowl": { + "Pr@50": NaN, + "Pr@60": NaN, + "Pr@70": NaN, + "Pr@80": NaN, + "Pr@90": NaN, + "IoU": NaN + }, + "cereal_box": { + "Pr@50": 88.89, + "Pr@60": 86.67, + "Pr@70": 62.22, + "Pr@80": 55.56, + "Pr@90": 42.22, + "IoU": 68.84 + }, + "coffee_mug": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "flashlight": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "food_bag": { + "Pr@50": 53.33, + "Pr@60": 53.33, + "Pr@70": 53.33, + "Pr@80": 53.33, + "Pr@90": 26.67, + "IoU": 55.51 + }, + "food_box": { + "Pr@50": 85.0, + "Pr@60": 75.0, + "Pr@70": 60.0, + "Pr@80": 35.0, + "Pr@90": 20.0, + "IoU": 64.66 + }, + "food_can": { + "Pr@50": 50.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 30.22 + }, + "glue_stick": { + "Pr@50": 20.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 29.11 + }, + "hand_towel": { + "Pr@50": 39.47, + "Pr@60": 39.47, + "Pr@70": 39.47, + "Pr@80": 39.47, + "Pr@90": 18.42, + "IoU": 32.37 + }, + "instant_noodles": { + "Pr@50": 27.78, + "Pr@60": 27.78, + "Pr@70": 27.78, + "Pr@80": 27.78, + "Pr@90": 0.0, + "IoU": 27.14 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 85.11, + "Pr@90": 51.06, + "IoU": 87.94 + }, + "kleenex": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 95.83, + "Pr@90": 41.67, + "IoU": 87.99 + }, + "lemon": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 40.0, + "Pr@80": 40.0, + "Pr@90": 20.0, + "IoU": 51.92 + }, + "lime": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "marker": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.61 + }, + "orange": { + "Pr@50": 77.27, + "Pr@60": 63.64, + "Pr@70": 54.55, + "Pr@80": 50.0, + "Pr@90": 0.0, + "IoU": 59.15 + }, + "peach": { + "Pr@50": 83.33, + "Pr@60": 83.33, + "Pr@70": 50.0, + "Pr@80": 44.44, + "Pr@90": 5.56, + "IoU": 75.4 + }, + "pear": { + "Pr@50": 72.73, + "Pr@60": 63.64, + "Pr@70": 63.64, + "Pr@80": 45.45, + "Pr@90": 0.0, + "IoU": 52.04 + }, + "potato": { + "Pr@50": 100.0, + "Pr@60": 88.24, + "Pr@70": 52.94, + "Pr@80": 17.65, + "Pr@90": 0.0, + "IoU": 73.65 + }, + "shampoo": { + "Pr@50": 40.0, + "Pr@60": 40.0, + "Pr@70": 12.0, + "Pr@80": 4.0, + "Pr@90": 0.0, + "IoU": 37.9 + }, + "soda_can": { + "Pr@50": 77.78, + "Pr@60": 77.78, + "Pr@70": 55.56, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 68.3 + }, + "sponge": { + "Pr@50": 15.38, + "Pr@60": 7.69, + "Pr@70": 7.69, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 20.23 + }, + "stapler": { + "Pr@50": NaN, + "Pr@60": NaN, + "Pr@70": NaN, + "Pr@80": NaN, + "Pr@90": NaN, + "IoU": NaN + }, + "tomato": { + "Pr@50": 33.33, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 22.76 + }, + "toothpaste": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 61.9 + }, + "_unknown_": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 13.19 + } +} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512.py b/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512.py new file mode 100644 index 0000000000..9125269084 --- /dev/null +++ b/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512.py @@ -0,0 +1,339 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512' diff --git a/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/bisenetv1_results.json b/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/bisenetv1_results.json new file mode 100644 index 0000000000..8769043726 --- /dev/null +++ b/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/bisenetv1_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50": 56.6, + "mPr@60": 49.49, + "mPr@70": 41.35, + "mPr@80": 29.18, + "mPr@90": 11.78, + "mIoU": 47.81, + "FPS": 97.55, + "Var FPS": 5.1289, + "Mem (MB)": 204.9, + "Var Mem": 0.49 +} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/clutter/clutter_data.json b/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/clutter/clutter_data.json new file mode 100644 index 0000000000..9e26dfeeb6 --- /dev/null +++ b/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/clutter/clutter_data.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/confusion_matrix/confusion.json b/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..d6bdef4147 --- /dev/null +++ b/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4962 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.003872557385362779 + }, + { + "gt_label": "_background_", + "pred_label": "ball", + "score": 0.002513241196093684 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.02256688937294819 + }, + { + "gt_label": "_background_", + "pred_label": "bell_pepper", + "score": 2.240631081212794e-05 + }, + { + "gt_label": "_background_", + "pred_label": "binder", + "score": 0.023276422548665573 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "cereal_box", + "score": 0.18400809315946534 + }, + { + "gt_label": "_background_", + "pred_label": "coffee_mug", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "flashlight", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "food_bag", + "score": 0.010646732020896126 + }, + { + "gt_label": "_background_", + "pred_label": "food_box", + "score": 0.03803471260358717 + }, + { + "gt_label": "_background_", + "pred_label": "food_can", + "score": 0.0018709269528126827 + }, + { + "gt_label": "_background_", + "pred_label": "glue_stick", + "score": 0.001086706074388205 + }, + { + "gt_label": "_background_", + "pred_label": "hand_towel", + "score": 0.005190795338142973 + }, + { + "gt_label": "_background_", + "pred_label": "instant_noodles", + "score": 0.015822589818497677 + }, + { + "gt_label": "_background_", + "pred_label": "keyboard", + "score": 0.09969687995856327 + }, + { + "gt_label": "_background_", + "pred_label": "kleenex", + "score": 0.026189242954242204 + }, + { + "gt_label": "_background_", + "pred_label": "lemon", + "score": 0.0029949768785544344 + }, + { + "gt_label": "_background_", + "pred_label": "lime", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "marker", + "score": 0.001292097256832711 + }, + { + "gt_label": "_background_", + "pred_label": "orange", + "score": 0.004735200351629705 + }, + { + "gt_label": "_background_", + "pred_label": "peach", + "score": 0.0028866797096291493 + }, + { + "gt_label": "_background_", + "pred_label": "pear", + "score": 0.0063521891152382715 + }, + { + "gt_label": "_background_", + "pred_label": "potato", + "score": 0.013652912054856622 + }, + { + "gt_label": "_background_", + "pred_label": "shampoo", + "score": 0.0008887836622144082 + }, + { + "gt_label": "_background_", + "pred_label": "soda_can", + "score": 0.007838474399109424 + }, + { + "gt_label": "_background_", + "pred_label": "sponge", + "score": 0.0005340170743557158 + }, + { + "gt_label": "_background_", + "pred_label": "stapler", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "tomato", + "score": 0.0001269690946020583 + }, + { + "gt_label": "_background_", + "pred_label": "toothpaste", + "score": 0.003219039986675714 + }, + { + "gt_label": "_background_", + "pred_label": "_unknown_", + "score": 0.0 + }, + { + "gt_label": "apple", + "pred_label": "_background_", + "score": 10.356034503824256 + }, + { + "gt_label": "apple", + "pred_label": "ball", + "score": 0.0 + }, + { + "gt_label": "apple", + "pred_label": "banana", + "score": 1.1020697407325954 + 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\ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/confusion_matrix/top_10_confusion.json b/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..fea13117d3 --- /dev/null +++ b/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "_unknown_", + "pred_label": "cereal_box", + "score": 73.82848474998646 + }, + { + "gt_label": "lime", + "pred_label": "apple", + "score": 73.28125 + }, + { + "gt_label": "coffee_mug", + "pred_label": "food_can", + "score": 61.019992816952 + }, + { + "gt_label": "hand_towel", + "pred_label": "cereal_box", + "score": 59.835296703633745 + }, + { + "gt_label": "instant_noodles", + "pred_label": "cereal_box", + "score": 56.82823788215884 + }, + { + "gt_label": "bell_pepper", + "pred_label": "apple", + "score": 41.462480857580395 + }, + { + "gt_label": "flashlight", + "pred_label": "food_can", + "score": 33.30708661417322 + }, + { + "gt_label": "lemon", + "pred_label": "pear", + "score": 32.71994923717825 + }, + { + "gt_label": "tomato", + "pred_label": "apple", + "score": 31.744840525328332 + }, + { + "gt_label": "marker", + "pred_label": "keyboard", + "score": 31.396524777052498 + } +] \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/flops/flops_20241107_135233.json b/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/flops/flops_20241107_135233.json new file mode 100644 index 0000000000..cd1db4ee0f --- /dev/null +++ b/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/flops/flops_20241107_135233.json @@ -0,0 +1,13 @@ +{ + "ori_shape": [ + 512, + 512 + ], + "pad_shape": [ + 512, + 512 + ], + "flops": "14.852G", + "params": "13.279M", + "compute_type": "direct: randomly generate a picture" +} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/show/vis_data/config.py b/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..9125269084 --- /dev/null +++ b/my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/show/vis_data/config.py @@ -0,0 +1,339 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid10_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid10_cat-512x512' diff --git a/my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_135241/20241107_135241.json b/my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_135241/20241107_135241.json new file mode 100644 index 0000000000..71708fe859 --- /dev/null +++ b/my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_135241/20241107_135241.json @@ -0,0 +1 @@ +{"mPr@50": 88.19, "mPr@60": 86.84, "mPr@70": 85.69, "mPr@80": 81.5, "mPr@90": 47.61, "mIoU": 79.01, "data_time": 0.7666878983525947, "time": 0.8671421981093907} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/benchmark/fps_20241107_135422.json b/my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/benchmark/fps_20241107_135422.json new file mode 100644 index 0000000000..ef323a5ba4 --- /dev/null +++ b/my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/benchmark/fps_20241107_135422.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512.py", + "unit": "img / s", + "overall_fps_1": 15.67, + "overall_memory_1": 584, + "overall_fps_2": 15.74, + "overall_memory_2": 585, + "overall_fps_3": 15.5, + "overall_memory_3": 583, + "overall_fps_4": 15.42, + "overall_memory_4": 582, + "overall_fps_5": 15.4, + "overall_memory_5": 584, + "overall_fps_6": 15.64, + "overall_memory_6": 585, + "overall_fps_7": 15.61, + "overall_memory_7": 582, + "overall_fps_8": 15.56, + "overall_memory_8": 582, + "overall_fps_9": 15.57, + "overall_memory_9": 582, + "overall_fps_10": 15.55, + "overall_memory_10": 581, + "FPS": 15.57, + "Var FPS": 0.0102, + "Mem (MB)": 583.0, + "Var Mem": 1.8 +} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/clutter/clutter_data.json b/my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/clutter/clutter_data.json new file mode 100644 index 0000000000..9e26dfeeb6 --- /dev/null +++ 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100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 15.79, + "IoU": 88.34 + }, + "bell_pepper": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 0.0, + "IoU": 89.35 + }, + "binder": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 50.0, + "IoU": 91.49 + }, + "bowl": { + "Pr@50": NaN, + "Pr@60": NaN, + "Pr@70": NaN, + "Pr@80": NaN, + "Pr@90": NaN, + "IoU": NaN + }, + "cereal_box": { + "Pr@50": 100.0, + "Pr@60": 93.33, + "Pr@70": 88.89, + "Pr@80": 86.67, + "Pr@90": 68.89, + "IoU": 89.0 + }, + "coffee_mug": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 25.0, + "IoU": 87.57 + }, + "flashlight": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 50.0, + "Pr@90": 0.0, + "IoU": 79.39 + }, + "food_bag": { + "Pr@50": 90.0, + "Pr@60": 90.0, + "Pr@70": 90.0, + "Pr@80": 90.0, + "Pr@90": 86.67, + "IoU": 85.79 + }, + "food_box": { + "Pr@50": 60.0, + "Pr@60": 45.0, + "Pr@70": 40.0, + "Pr@80": 40.0, + "Pr@90": 40.0, + "IoU": 61.8 + }, + "food_can": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 50.0, + "IoU": 88.57 + }, + "glue_stick": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 60.0, + "IoU": 91.22 + }, + "hand_towel": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 50.0, + "IoU": 43.85 + }, + "instant_noodles": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 94.44, + "Pr@90": 22.22, + "IoU": 88.69 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 97.87, + "Pr@90": 95.74, + "IoU": 94.02 + }, + "kleenex": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 91.67, + "Pr@80": 87.5, + "Pr@90": 87.5, + "IoU": 91.81 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 90.0, + "Pr@90": 30.0, + "IoU": 86.56 + }, + "lime": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "marker": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 33.33, + "Pr@80": 16.67, + "Pr@90": 0.0, + "IoU": 46.3 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 45.45, + "IoU": 89.71 + }, + "peach": { + "Pr@50": 94.44, + "Pr@60": 94.44, + "Pr@70": 94.44, + "Pr@80": 94.44, + "Pr@90": 44.44, + "IoU": 88.77 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 90.91, + "Pr@70": 90.91, + "Pr@80": 72.73, + "Pr@90": 72.73, + "IoU": 83.02 + }, + "potato": { + "Pr@50": 100.0, + "Pr@60": 94.12, + "Pr@70": 94.12, + "Pr@80": 94.12, + "Pr@90": 47.06, + "IoU": 87.44 + }, + "shampoo": { + "Pr@50": 88.0, + "Pr@60": 84.0, + "Pr@70": 84.0, + "Pr@80": 84.0, + "Pr@90": 36.0, + "IoU": 79.42 + }, + "soda_can": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 77.78, + "IoU": 46.18 + }, + "sponge": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 15.38, + "IoU": 88.92 + }, + "stapler": { + "Pr@50": NaN, + "Pr@60": NaN, + "Pr@70": NaN, + "Pr@80": NaN, + "Pr@90": NaN, + "IoU": NaN + }, + "tomato": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 66.67, + "IoU": 90.06 + }, + "toothpaste": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 33.33, + "Pr@80": 16.67, + "Pr@90": 16.67, + "IoU": 51.42 + }, + "_unknown_": { + "Pr@50": 80.0, + "Pr@60": 80.0, + "Pr@70": 80.0, + "Pr@80": 80.0, + "Pr@90": 60.0, + "IoU": 79.92 + } +} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512.py b/my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512.py new file mode 100644 index 0000000000..cdcc7f3277 --- /dev/null +++ b/my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512.py @@ -0,0 +1,438 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/mask2former_results.json b/my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/mask2former_results.json new file mode 100644 index 0000000000..9a3387b1b8 --- /dev/null +++ b/my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/mask2former_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50": 88.19, + "mPr@60": 86.84, + "mPr@70": 85.69, + "mPr@80": 81.5, + "mPr@90": 47.61, + "mIoU": 79.01, + "FPS": 15.57, + "Var FPS": 0.0102, + "Mem (MB)": 583.0, + "Var Mem": 1.8 +} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/show/vis_data/config.py b/my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..cdcc7f3277 --- /dev/null +++ b/my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/show/vis_data/config.py @@ -0,0 +1,438 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid10_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_135728/20241107_135728.json b/my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_135728/20241107_135728.json new file mode 100644 index 0000000000..e721c29d21 --- /dev/null +++ b/my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_135728/20241107_135728.json @@ -0,0 +1 @@ +{"mPr@50": 85.45, "mPr@60": 83.58, "mPr@70": 83.18, "mPr@80": 78.06, "mPr@90": 41.53, "mIoU": 76.42, "data_time": 0.7740982995174899, "time": 0.8572661805861067} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/benchmark/fps_20241107_135908.json b/my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/benchmark/fps_20241107_135908.json new file mode 100644 index 0000000000..4a552cfb8e --- /dev/null +++ b/my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/benchmark/fps_20241107_135908.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512.py", + "unit": "img / s", + "overall_fps_1": 23.24, + "overall_memory_1": 551, + "overall_fps_2": 23.69, + "overall_memory_2": 552, + "overall_fps_3": 23.54, + "overall_memory_3": 551, + "overall_fps_4": 23.02, + "overall_memory_4": 551, + "overall_fps_5": 23.03, + "overall_memory_5": 551, + "overall_fps_6": 22.5, + "overall_memory_6": 551, + "overall_fps_7": 23.13, + "overall_memory_7": 551, + "overall_fps_8": 22.87, + "overall_memory_8": 551, + "overall_fps_9": 22.98, + "overall_memory_9": 551, + "overall_fps_10": 23.06, + "overall_memory_10": 551, + "FPS": 23.11, + "Var FPS": 0.1004, + "Mem (MB)": 551.1, + "Var Mem": 0.09 +} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/clutter/clutter_data.json b/my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/clutter/clutter_data.json new file mode 100644 index 0000000000..9e26dfeeb6 --- /dev/null +++ b/my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/clutter/clutter_data.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/confusion_matrix/confusion.json b/my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..ceb45fa19e --- /dev/null +++ b/my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4962 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.00413022995970225 + }, + { + "gt_label": "_background_", + "pred_label": "ball", + "score": 0.001605785608202502 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.02235402942023297 + }, + { + "gt_label": "_background_", + "pred_label": "bell_pepper", + "score": 0.0008887836622144082 + }, + { + "gt_label": "_background_", + "pred_label": "binder", + "score": 0.007517317277468923 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "cereal_box", + "score": 0.0902638231066574 + }, + { + "gt_label": "_background_", + "pred_label": "coffee_mug", + "score": 0.000619907932468873 + }, + { + "gt_label": "_background_", + "pred_label": "flashlight", + "score": 0.0 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+ "Pr@60": 80.0, + "Pr@70": 80.0, + "Pr@80": 40.0, + "Pr@90": 0.0, + "IoU": 78.07 + } +} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/maskformer_results.json b/my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/maskformer_results.json new file mode 100644 index 0000000000..cf53d81205 --- /dev/null +++ b/my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/maskformer_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50": 85.45, + "mPr@60": 83.58, + "mPr@70": 83.18, + "mPr@80": 78.06, + "mPr@90": 41.53, + "mIoU": 76.42, + "FPS": 23.11, + "Var FPS": 0.1004, + "Mem (MB)": 551.1, + "Var Mem": 0.09 +} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512.py b/my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512.py new file mode 100644 index 0000000000..e2a12d69b3 --- /dev/null +++ b/my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512.py @@ -0,0 +1,528 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/show/vis_data/config.py b/my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..e2a12d69b3 --- /dev/null +++ b/my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/show/vis_data/config.py @@ -0,0 +1,528 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid10_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_140127/20241107_140127.json b/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_140127/20241107_140127.json new file mode 100644 index 0000000000..248920368c --- /dev/null +++ b/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/20241107_140127/20241107_140127.json @@ -0,0 +1 @@ +{"mPr@50": 83.71, "mPr@60": 79.92, "mPr@70": 69.98, "mPr@80": 48.03, "mPr@90": 12.69, "mIoU": 72.11, "data_time": 0.7877853695708926, "time": 0.8363837629261583} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/benchmark/fps_20241107_140303.json b/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/benchmark/fps_20241107_140303.json new file mode 100644 index 0000000000..11014c95dd --- /dev/null +++ b/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/benchmark/fps_20241107_140303.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/segformer_mit-b0_1xb2-10k_arid10_cat-512x512.py", + "unit": "img / s", + "overall_fps_1": 68.21, + "overall_memory_1": 355, + "overall_fps_2": 68.01, + "overall_memory_2": 355, + "overall_fps_3": 67.03, + "overall_memory_3": 356, + "overall_fps_4": 67.51, + "overall_memory_4": 355, + "overall_fps_5": 66.66, + "overall_memory_5": 355, + "overall_fps_6": 65.47, + "overall_memory_6": 354, + "overall_fps_7": 67.32, + "overall_memory_7": 354, + "overall_fps_8": 68.13, + "overall_memory_8": 354, + "overall_fps_9": 68.13, + "overall_memory_9": 354, + "overall_fps_10": 68.15, + "overall_memory_10": 354, + "FPS": 67.46, + "Var FPS": 0.7055, + "Mem (MB)": 354.6, + "Var Mem": 0.44 +} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/clutter/clutter_data.json b/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/clutter/clutter_data.json new file mode 100644 index 0000000000..9e26dfeeb6 --- /dev/null +++ b/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/clutter/clutter_data.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/confusion_matrix/confusion.json b/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..32ddcab5eb --- /dev/null +++ b/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4962 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.006180407399011957 + }, + { + "gt_label": 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"pred_label": "keyboard", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "kleenex", + "score": 0.17335716994420067 + }, + { + "gt_label": "_unknown_", + "pred_label": "lemon", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "lime", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "marker", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "orange", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "peach", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "pear", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "potato", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "shampoo", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "soda_can", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "sponge", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "stapler", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "tomato", + "score": 0.0 + }, + { + "gt_label": "_unknown_", + "pred_label": "toothpaste", + "score": 0.0 + } +] \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/confusion_matrix/top_10_confusion.json b/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..cf0ac07d06 --- /dev/null +++ b/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "lime", + "pred_label": "pear", + "score": 81.25 + }, + { + "gt_label": "flashlight", + "pred_label": "marker", + "score": 37.874015748031496 + }, + { + "gt_label": "marker", + "pred_label": "keyboard", + "score": 31.267812816033835 + }, + { + "gt_label": "hand_towel", + "pred_label": "cereal_box", + "score": 16.9380624615015 + }, + { + "gt_label": "_unknown_", + "pred_label": "cereal_box", + "score": 14.627011214041929 + }, + { + "gt_label": "binder", + "pred_label": "cereal_box", + "score": 13.43982565553057 + }, + { + "gt_label": "shampoo", + "pred_label": "cereal_box", + "score": 10.341162077057302 + }, + { + "gt_label": "lime", + "pred_label": "apple", + "score": 9.921875 + }, + { + "gt_label": "glue_stick", + "pred_label": "ball", + "score": 9.709406417848781 + }, + { + "gt_label": "flashlight", + "pred_label": "food_can", + "score": 9.26509186351706 + } +] \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/flops/flops_20241107_140425.json b/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/flops/flops_20241107_140425.json new file mode 100644 index 0000000000..689019ad34 --- /dev/null +++ b/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/flops/flops_20241107_140425.json @@ -0,0 +1,13 @@ +{ + "ori_shape": [ + 512, + 512 + ], + "pad_shape": [ + 512, + 512 + ], + "flops": "8.011G", + "params": "3.723M", + "compute_type": "direct: randomly generate a picture" +} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/segformer_label_results.json b/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/segformer_label_results.json new file mode 100644 index 0000000000..244de93baa --- /dev/null +++ b/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/segformer_label_results.json @@ -0,0 +1,258 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 98.93 + }, + "apple": { + "Pr@50": 100.0, + "Pr@60": 95.45, + "Pr@70": 95.45, + "Pr@80": 77.27, + "Pr@90": 9.09, + "IoU": 83.52 + }, + "ball": { + "Pr@50": 87.5, + "Pr@60": 87.5, + "Pr@70": 68.75, + "Pr@80": 43.75, + "Pr@90": 0.0, + "IoU": 72.2 + }, + "banana": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 94.74, + "Pr@90": 0.0, + "IoU": 87.2 + }, + "bell_pepper": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 50.0, + "Pr@90": 0.0, + "IoU": 79.27 + }, + "binder": { + "Pr@50": 72.22, + "Pr@60": 55.56, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 27.78, + "IoU": 75.38 + }, + "bowl": { + "Pr@50": NaN, + "Pr@60": NaN, + "Pr@70": NaN, + "Pr@80": NaN, + "Pr@90": NaN, + "IoU": NaN + }, + "cereal_box": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 93.33, + "Pr@80": 82.22, + "Pr@90": 57.78, + "IoU": 83.6 + }, + "coffee_mug": { + "Pr@50": 87.5, + "Pr@60": 75.0, + "Pr@70": 12.5, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 64.03 + }, + "flashlight": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 26.7 + }, + "food_bag": { + "Pr@50": 100.0, + "Pr@60": 96.67, + "Pr@70": 76.67, + "Pr@80": 76.67, + "Pr@90": 3.33, + "IoU": 78.09 + }, + "food_box": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 75.0, + "Pr@90": 25.0, + "IoU": 85.31 + }, + "food_can": { + "Pr@50": 75.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 25.0, + "Pr@90": 0.0, + "IoU": 69.62 + }, + "glue_stick": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 80.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 73.84 + }, + "hand_towel": { + "Pr@50": 76.32, + "Pr@60": 76.32, + "Pr@70": 57.89, + "Pr@80": 50.0, + "Pr@90": 28.95, + "IoU": 65.81 + }, + "instant_noodles": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 38.89, + "Pr@90": 0.0, + "IoU": 79.35 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 91.49, + "Pr@90": 55.32, + "IoU": 89.88 + }, + "kleenex": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 91.67, + "Pr@80": 70.83, + "Pr@90": 25.0, + "IoU": 83.79 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 90.0, + "Pr@90": 10.0, + "IoU": 85.06 + }, + "lime": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "marker": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 31.3 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 90.91, + "Pr@70": 81.82, + "Pr@80": 72.73, + "Pr@90": 0.0, + "IoU": 80.53 + }, + "peach": { + "Pr@50": 94.44, + "Pr@60": 88.89, + "Pr@70": 83.33, + "Pr@80": 66.67, + "Pr@90": 0.0, + "IoU": 81.2 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 90.91, + "Pr@70": 90.91, + "Pr@80": 81.82, + "Pr@90": 9.09, + "IoU": 80.99 + }, + "potato": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 94.12, + "Pr@80": 76.47, + "Pr@90": 29.41, + "IoU": 86.52 + }, + "shampoo": { + "Pr@50": 56.0, + "Pr@60": 48.0, + "Pr@70": 32.0, + "Pr@80": 12.0, + "Pr@90": 0.0, + "IoU": 57.61 + }, + "soda_can": { + "Pr@50": 77.78, + "Pr@60": 77.78, + "Pr@70": 77.78, + "Pr@80": 66.67, + "Pr@90": 0.0, + "IoU": 72.91 + }, + "sponge": { + "Pr@50": 84.62, + "Pr@60": 84.62, + "Pr@70": 23.08, + "Pr@80": 15.38, + "Pr@90": 0.0, + "IoU": 69.38 + }, + "stapler": { + "Pr@50": NaN, + "Pr@60": NaN, + "Pr@70": NaN, + "Pr@80": NaN, + "Pr@90": NaN, + "IoU": NaN + }, + "tomato": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 33.33, + "Pr@90": 0.0, + "IoU": 76.57 + }, + "toothpaste": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 76.34 + }, + "_unknown_": { + "Pr@50": 100.0, + "Pr@60": 80.0, + "Pr@70": 40.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 68.3 + } +} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/segformer_mit-b0_1xb2-10k_arid10_cat-512x512.py b/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/segformer_mit-b0_1xb2-10k_arid10_cat-512x512.py new file mode 100644 index 0000000000..1b9862e597 --- /dev/null +++ b/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/segformer_mit-b0_1xb2-10k_arid10_cat-512x512.py @@ -0,0 +1,325 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512' diff --git a/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/segformer_results.json b/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/segformer_results.json new file mode 100644 index 0000000000..4187812910 --- /dev/null +++ b/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/segformer_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50": 83.71, + "mPr@60": 79.92, + "mPr@70": 69.98, + "mPr@80": 48.03, + "mPr@90": 12.69, + "mIoU": 72.11, + "FPS": 67.46, + "Var FPS": 0.7055, + "Mem (MB)": 354.6, + "Var Mem": 0.44 +} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/show/vis_data/config.py b/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..1b9862e597 --- /dev/null +++ b/my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/show/vis_data/config.py @@ -0,0 +1,325 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid10_cat/segformer_mit-b0_1xb2-10k_arid10_cat-512x512' diff --git a/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_140432/20241107_140432.json b/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_140432/20241107_140432.json new file mode 100644 index 0000000000..a7a7d574e7 --- /dev/null +++ b/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/20241107_140432/20241107_140432.json @@ -0,0 +1 @@ +{"mPr@50": 90.23, "mPr@60": 84.62, "mPr@70": 82.15, "mPr@80": 64.71, "mPr@90": 19.2, "mIoU": 76.48, "data_time": 0.7550744467442578, "time": 0.7984591002511506} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/benchmark/fps_20241107_140604.json b/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/benchmark/fps_20241107_140604.json new file mode 100644 index 0000000000..5ac245d36d --- /dev/null +++ b/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/benchmark/fps_20241107_140604.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512.py", + "unit": "img / s", + "overall_fps_1": 56.91, + "overall_memory_1": 191, + "overall_fps_2": 56.94, + "overall_memory_2": 192, + "overall_fps_3": 56.74, + "overall_memory_3": 192, + "overall_fps_4": 56.72, + "overall_memory_4": 192, + "overall_fps_5": 56.72, + "overall_memory_5": 192, + "overall_fps_6": 56.71, + "overall_memory_6": 192, + "overall_fps_7": 56.63, + "overall_memory_7": 192, + "overall_fps_8": 53.12, + "overall_memory_8": 192, + "overall_fps_9": 55.71, + "overall_memory_9": 192, + "overall_fps_10": 55.63, + "overall_memory_10": 192, + "FPS": 56.18, + "Var FPS": 1.2383, + "Mem (MB)": 191.9, + "Var Mem": 0.09 +} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/clutter/clutter_data.json b/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/clutter/clutter_data.json new file mode 100644 index 0000000000..9e26dfeeb6 --- /dev/null +++ b/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/clutter/clutter_data.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/confusion_matrix/confusion.json b/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..07c1f66a5c --- /dev/null +++ b/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4962 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.004802419284066088 + }, + { + "gt_label": "_background_", + "pred_label": "ball", + "score": 0.0016580670000974674 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.029225298069285543 + }, + { + "gt_label": "_background_", + "pred_label": "bell_pepper", + "score": 0.0002875476554223085 + }, + { + "gt_label": "_background_", + "pred_label": "binder", + "score": 0.009011071331610786 + }, + { + "gt_label": "_background_", + 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"pred_label": "keyboard", + "score": 0.09434177167446468 + }, + { + "gt_label": "_background_", + "pred_label": "kleenex", + "score": 0.020288914440381847 + }, + { + "gt_label": "_background_", + "pred_label": "lemon", + "score": 0.0107064821830618 + }, + { + "gt_label": "_background_", + "pred_label": "lime", + "score": 0.0 + }, + { + "gt_label": "_background_", + "pred_label": "marker", + "score": 0.0007767521081537686 + }, + { + "gt_label": "_background_", + "pred_label": "orange", + "score": 0.004529809169185198 + }, + { + "gt_label": "_background_", + "pred_label": "peach", + "score": 0.006270032642260468 + }, + { + "gt_label": "_background_", + "pred_label": "pear", + "score": 0.0061692042436058935 + }, + { + "gt_label": "_background_", + "pred_label": "potato", + "score": 0.008529335649150035 + }, + { + "gt_label": "_background_", + "pred_label": "shampoo", + "score": 0.009276212676220965 + }, + { + "gt_label": "_background_", + "pred_label": "soda_can", + "score": 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file mode 100644 index 0000000000..953abe62cc --- /dev/null +++ b/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "lime", + "pred_label": "pear", + "score": 91.25 + }, + { + "gt_label": "marker", + "pred_label": "keyboard", + "score": 31.994116024639148 + }, + { + "gt_label": "_unknown_", + "pred_label": "cereal_box", + "score": 21.864673059212308 + }, + { + "gt_label": "marker", + "pred_label": "kleenex", + "score": 13.211363427415648 + }, + { + "gt_label": "binder", + "pred_label": "cereal_box", + "score": 11.198386386293556 + }, + { + "gt_label": "shampoo", + "pred_label": "keyboard", + "score": 11.13339617721976 + }, + { + "gt_label": "toothpaste", + "pred_label": "keyboard", + "score": 10.043637387387387 + }, + { + "gt_label": "hand_towel", + "pred_label": "cereal_box", + "score": 9.8006640272563 + }, + { + "gt_label": "flashlight", + "pred_label": "peach", + "score": 8.608923884514436 + }, + { + "gt_label": "coffee_mug", + "pred_label": "ball", + "score": 8.08691488088112 + } +] \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/flops/flops_20241107_140732.json b/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/flops/flops_20241107_140732.json new file mode 100644 index 0000000000..dd3c61e05c --- /dev/null +++ b/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/flops/flops_20241107_140732.json @@ -0,0 +1,13 @@ +{ + "ori_shape": [ + 512, + 512 + ], + "pad_shape": [ + 512, + 512 + ], + "flops": "6.327G", + "params": "4.233M", + "compute_type": "direct: randomly generate a picture" +} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/segnext_label_results.json 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+ "toothpaste": { + "Pr@50": 100.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 66.43 + }, + "_unknown_": { + "Pr@50": 80.0, + "Pr@60": 80.0, + "Pr@70": 80.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 66.1 + } +} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512.py b/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512.py new file mode 100644 index 0000000000..1db1a5d81e --- /dev/null +++ b/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512.py @@ -0,0 +1,347 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/segnext_results.json b/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/segnext_results.json new file mode 100644 index 0000000000..84751b448d --- /dev/null +++ b/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/segnext_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50": 90.23, + "mPr@60": 84.62, + "mPr@70": 82.15, + "mPr@80": 64.71, + "mPr@90": 19.2, + "mIoU": 76.48, + "FPS": 56.18, + "Var FPS": 1.2383, + "Mem (MB)": 191.9, + "Var Mem": 0.09 +} \ No newline at end of file diff --git a/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/show/vis_data/config.py b/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..1db1a5d81e --- /dev/null +++ b/my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/show/vis_data/config.py @@ -0,0 +1,347 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID10_CAT/' +dataset_type = 'ARID10CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID10_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID10CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid10_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid10_cat-512x512' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20240918_212416/20240918_212416.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20240918_212416/20240918_212416.json new file mode 100644 index 0000000000..74d29dd763 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/20240918_212416/20240918_212416.json @@ -0,0 +1 @@ +{"mPr@50.0": 85.76, "mPr@60.0": 79.66, "mPr@70.0": 70.07, "mPr@80.0": 52.83, "mPr@90.0": 20.88, "mIoU": 70.84, "data_time": 0.811857026257961, "time": 0.844741730381259} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/benchmark/fps_20240918_213022.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/benchmark/fps_20240918_213022.json new file mode 100644 index 0000000000..d7eb4fbecc --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/benchmark/fps_20240918_213022.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py", + "unit": "img / s", + "overall_fps_1": 94.88, + "overall_memory_1": 204, + "overall_fps_2": 95.78, + "overall_memory_2": 206, + "overall_fps_3": 94.7, + "overall_memory_3": 205, + "overall_fps_4": 95.22, + "overall_memory_4": 205, + "overall_fps_5": 95.53, + "overall_memory_5": 206, + "overall_fps_6": 95.14, + "overall_memory_6": 204, + "overall_fps_7": 95.01, + "overall_memory_7": 205, + "overall_fps_8": 94.51, + "overall_memory_8": 205, + "overall_fps_9": 94.79, + "overall_memory_9": 204, + "overall_fps_10": 94.66, + "overall_memory_10": 205, + "average_fps": 95.02, + "fps_variance": 0.1451, + "average_mem": 204.9, + "mem_variance": 0.49 +} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_label_results.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_label_results.json new file mode 100644 index 0000000000..22f6887fa8 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_label_results.json @@ -0,0 +1,258 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 98.58 + }, + "apple": { + "Pr@50": 87.1, + "Pr@60": 35.48, + "Pr@70": 9.68, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 53.94 + }, + "ball": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 60.87, + "Pr@90": 0.0, + "IoU": 80.98 + }, + "banana": { + "Pr@50": 100.0, + "Pr@60": 64.29, + "Pr@70": 64.29, + "Pr@80": 28.57, + "Pr@90": 0.0, + "IoU": 72.13 + }, + "bell_pepper": { + "Pr@50": 40.0, + "Pr@60": 26.67, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 45.34 + }, + "binder": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 92.11, + "Pr@90": 86.84, + "IoU": 94.53 + }, + "bowl": { + "Pr@50": 80.0, + "Pr@60": 80.0, + "Pr@70": 80.0, + "Pr@80": 80.0, + "Pr@90": 0.0, + "IoU": 84.55 + }, + "cereal_box": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 98.99, + "Pr@80": 88.89, + "Pr@90": 79.8, + "IoU": 90.73 + }, + "coffee_mug": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 95.0, + "Pr@80": 90.0, + "Pr@90": 16.67, + "IoU": 85.46 + }, + "flashlight": { + "Pr@50": 100.0, + "Pr@60": 86.49, + "Pr@70": 70.27, + "Pr@80": 51.35, + "Pr@90": 0.0, + "IoU": 77.6 + }, + "food_bag": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 75.0, + "IoU": 91.58 + }, + "food_box": { + "Pr@50": 100.0, + "Pr@60": 96.34, + "Pr@70": 86.59, + "Pr@80": 71.95, + "Pr@90": 39.02, + "IoU": 86.17 + }, + "food_can": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 82.35, + "Pr@80": 38.24, + "Pr@90": 0.0, + "IoU": 75.83 + }, + "glue_stick": { + "Pr@50": 96.43, + "Pr@60": 91.07, + "Pr@70": 89.29, + "Pr@80": 67.86, + "Pr@90": 0.0, + "IoU": 76.66 + }, + "hand_towel": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 80.0, + "IoU": 95.33 + }, + "instant_noodles": { + "Pr@50": 97.22, + "Pr@60": 97.22, + "Pr@70": 97.22, + "Pr@80": 86.11, + "Pr@90": 36.11, + "IoU": 86.34 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 96.08, + "Pr@80": 90.2, + "Pr@90": 86.27, + "IoU": 93.31 + }, + "kleenex": { + "Pr@50": 81.01, + "Pr@60": 81.01, + "Pr@70": 81.01, + "Pr@80": 75.95, + "Pr@90": 53.16, + "IoU": 75.09 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 87.5, + "Pr@90": 0.0, + "IoU": 47.82 + }, + "lime": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 8.2 + }, + "marker": { + "Pr@50": 62.67, + "Pr@60": 53.33, + "Pr@70": 48.0, + "Pr@80": 26.67, + "Pr@90": 0.0, + "IoU": 46.25 + }, + "orange": { + "Pr@50": 77.78, + "Pr@60": 77.78, + "Pr@70": 77.78, + "Pr@80": 72.22, + "Pr@90": 5.56, + "IoU": 73.49 + }, + "peach": { + "Pr@50": 96.15, + "Pr@60": 69.23, + "Pr@70": 57.69, + "Pr@80": 30.77, + "Pr@90": 0.0, + "IoU": 61.7 + }, + "pear": { + "Pr@50": 70.97, + "Pr@60": 70.97, + "Pr@70": 48.39, + "Pr@80": 16.13, + "Pr@90": 9.68, + "IoU": 57.42 + }, + "potato": { + "Pr@50": 100.0, + "Pr@60": 83.33, + "Pr@70": 33.33, + "Pr@80": 16.67, + "Pr@90": 0.0, + "IoU": 66.83 + }, + "shampoo": { + "Pr@50": 92.59, + "Pr@60": 92.59, + "Pr@70": 66.67, + "Pr@80": 33.33, + "Pr@90": 0.0, + "IoU": 71.27 + }, + "soda_can": { + "Pr@50": 100.0, + "Pr@60": 95.83, + "Pr@70": 87.5, + "Pr@80": 29.17, + "Pr@90": 0.0, + "IoU": 76.44 + }, + "sponge": { + "Pr@50": 62.5, + "Pr@60": 62.5, + "Pr@70": 25.0, + "Pr@80": 12.5, + "Pr@90": 0.0, + "IoU": 62.44 + }, + "stapler": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 82.0, + "Pr@80": 48.0, + "Pr@90": 0.0, + "IoU": 80.85 + }, + "tomato": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "toothpaste": { + "Pr@50": 100.0, + "Pr@60": 85.0, + "Pr@70": 65.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 66.43 + }, + "_unknown_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 95.45, + "Pr@90": 0.0, + "IoU": 83.48 + } +} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..6e6a41297f --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,339 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_results.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_results.json new file mode 100644 index 0000000000..2c8d853d09 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/bisenetv1_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50.0": 85.76, + "mPr@60.0": 79.66, + "mPr@70.0": 70.07, + "mPr@80.0": 52.83, + "mPr@90.0": 20.88, + "mIoU": 70.84, + "average_fps": 95.02, + "fps_variance": 0.1451, + "average_mem": 204.9, + "mem_variance": 0.49 +} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/1/20240918_212622/20240918_212622.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/1/20240918_212622/20240918_212622.json new file mode 100644 index 0000000000..a612eeeb62 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/1/20240918_212622/20240918_212622.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 100.0, "mPr@80.0": 100.0, "mPr@90.0": 60.0, "mIoU": 77.5, "data_time": 0.0121384064356486, "time": 0.342536727587382} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/1/20240918_212622/vis_data/config.py b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/1/20240918_212622/vis_data/config.py new file mode 100644 index 0000000000..3ec396c26d --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/1/20240918_212622/vis_data/config.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/9', seg_map_path='ann_dir/9'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/9' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..2647cada4d --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/1', seg_map_path='ann_dir/1'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/1' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/10/20240918_212628/20240918_212628.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/10/20240918_212628/20240918_212628.json new file mode 100644 index 0000000000..5c893665f8 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/10/20240918_212628/20240918_212628.json @@ -0,0 +1 @@ +{"mPr@50.0": 84.81, "mPr@60.0": 80.0, "mPr@70.0": 72.31, "mPr@80.0": 63.65, "mPr@90.0": 24.1, "mIoU": 70.11, "data_time": 0.013056959424700056, "time": 0.029277188437325612} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..851ec49430 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/10/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/10', seg_map_path='ann_dir/10'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/10' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/11/20240918_212640/20240918_212640.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/11/20240918_212640/20240918_212640.json new file mode 100644 index 0000000000..a850884ce9 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/11/20240918_212640/20240918_212640.json @@ -0,0 +1 @@ +{"mPr@50.0": 84.81, "mPr@60.0": 80.0, "mPr@70.0": 72.31, "mPr@80.0": 53.4, "mPr@90.0": 26.41, "mIoU": 67.25, "data_time": 0.015907560076032366, "time": 0.03202813012259347} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/11/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/11/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..06f0df66ee --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/11/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/11', seg_map_path='ann_dir/11'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/11' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/12/20240918_212652/20240918_212652.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/12/20240918_212652/20240918_212652.json new file mode 100644 index 0000000000..38d8f4cdf0 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/12/20240918_212652/20240918_212652.json @@ -0,0 +1 @@ +{"mPr@50.0": 87.68, "mPr@60.0": 85.89, "mPr@70.0": 77.86, "mPr@80.0": 59.7, "mPr@90.0": 23.81, "mIoU": 69.95, "data_time": 0.013392823083060128, "time": 0.02980405943734305} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/12/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/12/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..409ecd8c0b --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/12/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/12', seg_map_path='ann_dir/12'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/12' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/13/20240918_212704/20240918_212704.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/13/20240918_212704/20240918_212704.json new file mode 100644 index 0000000000..893214b2d8 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/13/20240918_212704/20240918_212704.json @@ -0,0 +1 @@ +{"mPr@50.0": 82.99, "mPr@60.0": 79.54, "mPr@70.0": 77.13, "mPr@80.0": 55.75, "mPr@90.0": 22.13, "mIoU": 69.27, "data_time": 0.012537240982055664, "time": 0.02847276415143694} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/13/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/13/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..497aa10d2c --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/13/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/13', seg_map_path='ann_dir/13'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/13' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/14/20240918_212717/20240918_212717.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/14/20240918_212717/20240918_212717.json new file mode 100644 index 0000000000..80208e484f --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/14/20240918_212717/20240918_212717.json @@ -0,0 +1 @@ +{"mPr@50.0": 85.22, "mPr@60.0": 78.56, "mPr@70.0": 73.44, "mPr@80.0": 44.17, "mPr@90.0": 20.89, "mIoU": 70.84, "data_time": 0.012105192456926619, "time": 0.028067384447370256} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/14/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/14/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..73d74d5895 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/14/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/14', seg_map_path='ann_dir/14'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/14' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/15/20240918_212729/20240918_212729.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/15/20240918_212729/20240918_212729.json new file mode 100644 index 0000000000..6e77b49d96 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/15/20240918_212729/20240918_212729.json @@ -0,0 +1 @@ +{"mPr@50.0": 85.22, "mPr@60.0": 79.28, "mPr@70.0": 71.78, "mPr@80.0": 55.28, "mPr@90.0": 20.22, "mIoU": 72.1, "data_time": 0.01270331655229841, "time": 0.02898672648838588} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/15/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/15/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..d9603b23c4 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/15/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/15', seg_map_path='ann_dir/15'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/15' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/16/20240918_212741/20240918_212741.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/16/20240918_212741/20240918_212741.json new file mode 100644 index 0000000000..83d654ca4d --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/16/20240918_212741/20240918_212741.json @@ -0,0 +1 @@ +{"mPr@50.0": 85.22, "mPr@60.0": 77.61, "mPr@70.0": 66.5, "mPr@80.0": 50.83, "mPr@90.0": 17.33, "mIoU": 66.75, "data_time": 0.012563160487583705, "time": 0.028674023491995677} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/16/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/16/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..fa75c445e6 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/16/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/16', seg_map_path='ann_dir/16'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/16' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/17/20240918_212753/20240918_212753.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/17/20240918_212753/20240918_212753.json new file mode 100644 index 0000000000..3eea0d0e57 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/17/20240918_212753/20240918_212753.json @@ -0,0 +1 @@ +{"mPr@50.0": 85.83, "mPr@60.0": 76.72, "mPr@70.0": 65.26, "mPr@80.0": 47.55, "mPr@90.0": 17.03, "mIoU": 69.2, "data_time": 0.018230063574654714, "time": 0.033932277134486606} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/17/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/17/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..1b196e6532 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/17/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/17', seg_map_path='ann_dir/17'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/17' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/18/20240918_212805/20240918_212805.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/18/20240918_212805/20240918_212805.json new file mode 100644 index 0000000000..549eba3030 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/18/20240918_212805/20240918_212805.json @@ -0,0 +1 @@ +{"mPr@50.0": 85.83, "mPr@60.0": 78.02, "mPr@70.0": 59.79, "mPr@80.0": 39.9, "mPr@90.0": 17.03, "mIoU": 68.28, "data_time": 0.017142023359026228, "time": 0.03334386008126395} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/18/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/18/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..6199a6e33a --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/18/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/18', seg_map_path='ann_dir/18'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/18' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/19/20240918_212817/20240918_212817.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/19/20240918_212817/20240918_212817.json new file mode 100644 index 0000000000..15a148e49c --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/19/20240918_212817/20240918_212817.json @@ -0,0 +1 @@ +{"mPr@50.0": 82.6, "mPr@60.0": 75.83, "mPr@70.0": 58.8, "mPr@80.0": 42.14, "mPr@90.0": 14.95, "mIoU": 66.84, "data_time": 0.012810059956141881, "time": 0.02917957305908203} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/19/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/19/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..ad5fced713 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/19/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/19', seg_map_path='ann_dir/19'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/19' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/2/20240918_212829/20240918_212829.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/2/20240918_212829/20240918_212829.json new file mode 100644 index 0000000000..89c7e0859c --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/2/20240918_212829/20240918_212829.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 100.0, "mPr@80.0": 90.0, "mPr@90.0": 60.0, "mIoU": 91.15, "data_time": 0.01359520639692034, "time": 0.02981257438659668} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/2/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/2/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..620e3a39a9 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/2/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/2', seg_map_path='ann_dir/2'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/2' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/20/20240918_212841/20240918_212841.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/20/20240918_212841/20240918_212841.json new file mode 100644 index 0000000000..3cc2cb2724 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/20/20240918_212841/20240918_212841.json @@ -0,0 +1 @@ +{"mPr@50.0": 80.99, "mPr@60.0": 74.11, "mPr@70.0": 57.86, "mPr@80.0": 38.49, "mPr@90.0": 12.34, "mIoU": 66.58, "data_time": 0.019823551177978516, "time": 0.03635515485491071} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/20/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/20/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..58bdc3037e --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/20/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/20', seg_map_path='ann_dir/20'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/20' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/3/20240918_212853/20240918_212853.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/3/20240918_212853/20240918_212853.json new file mode 100644 index 0000000000..d2bc92db41 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/3/20240918_212853/20240918_212853.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 100.0, "mPr@80.0": 88.46, "mPr@90.0": 50.0, "mIoU": 90.52, "data_time": 0.019475085394723073, "time": 0.03672228540693011} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/3/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/3/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..6846ba307c --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/3/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/3', seg_map_path='ann_dir/3'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/3' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/4/20240918_212906/20240918_212906.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/4/20240918_212906/20240918_212906.json new file mode 100644 index 0000000000..db466b1341 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/4/20240918_212906/20240918_212906.json @@ -0,0 +1 @@ +{"mPr@50.0": 93.33, "mPr@60.0": 93.33, "mPr@70.0": 93.33, "mPr@80.0": 80.0, "mPr@90.0": 43.33, "mIoU": 80.92, "data_time": 0.019700118473597934, "time": 0.03665954726082938} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/4/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/4/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..ac3c888679 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/4/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/4', seg_map_path='ann_dir/4'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/4' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/5/20240918_212918/20240918_212918.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/5/20240918_212918/20240918_212918.json new file mode 100644 index 0000000000..5db0a964bd --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/5/20240918_212918/20240918_212918.json @@ -0,0 +1 @@ +{"mPr@50.0": 93.75, "mPr@60.0": 93.75, "mPr@70.0": 91.67, "mPr@80.0": 76.04, "mPr@90.0": 37.5, "mIoU": 80.76, "data_time": 0.020836591720581055, "time": 0.03700034958975656} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/5/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/5/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..32c59c39d5 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/5/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/5', seg_map_path='ann_dir/5'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/5' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/6/20240918_212930/20240918_212930.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/6/20240918_212930/20240918_212930.json new file mode 100644 index 0000000000..46df2ceaf7 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/6/20240918_212930/20240918_212930.json @@ -0,0 +1 @@ +{"mPr@50.0": 91.67, "mPr@60.0": 91.67, "mPr@70.0": 89.04, "mPr@80.0": 81.14, "mPr@90.0": 33.33, "mIoU": 72.12, "data_time": 0.020194360188075473, "time": 0.036336353846958706} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/6/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/6/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..a8ac9eddce --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/6/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/6', seg_map_path='ann_dir/6'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/6' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/7/20240918_212942/20240918_212942.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/7/20240918_212942/20240918_212942.json new file mode 100644 index 0000000000..7d9deb87cb --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/7/20240918_212942/20240918_212942.json @@ -0,0 +1 @@ +{"mPr@50.0": 87.7, "mPr@60.0": 85.32, "mPr@70.0": 82.94, "mPr@80.0": 71.03, "mPr@90.0": 28.97, "mIoU": 65.69, "data_time": 0.018043347767421176, "time": 0.033972978591918945} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/7/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/7/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..1145fcb35f --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/7/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/7', seg_map_path='ann_dir/7'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/7' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/8/20240918_212954/20240918_212954.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/8/20240918_212954/20240918_212954.json new file mode 100644 index 0000000000..6f0f29ef8c --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/8/20240918_212954/20240918_212954.json @@ -0,0 +1 @@ +{"mPr@50.0": 89.67, "mPr@60.0": 81.67, "mPr@70.0": 77.67, "mPr@80.0": 67.67, "mPr@90.0": 30.33, "mIoU": 68.33, "data_time": 0.01836630276271275, "time": 0.034571477345057895} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/8/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/8/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..d376fb229c --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/8/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/8', seg_map_path='ann_dir/8'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/8' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/9/20240918_213006/20240918_213006.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/9/20240918_213006/20240918_213006.json new file mode 100644 index 0000000000..0840969557 --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/9/20240918_213006/20240918_213006.json @@ -0,0 +1 @@ +{"mPr@50.0": 90.2, "mPr@60.0": 86.2, "mPr@70.0": 76.2, "mPr@80.0": 67.87, "mPr@90.0": 28.53, "mIoU": 69.28, "data_time": 0.018638678959437778, "time": 0.0363835266658238} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/9/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/9/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..3ec396c26d --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/9/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512.py @@ -0,0 +1,333 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/9', seg_map_path='ann_dir/9'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/9' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/clutter_data.json b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/clutter_data.json new file mode 100644 index 0000000000..a29cae21db --- /dev/null +++ b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/clutter/clutter_data.json @@ -0,0 +1,202 @@ +{ + "1": { + "mPr@50.0": 100.0, + "mPr@60.0": 100.0, + "mPr@70.0": 100.0, + "mPr@80.0": 100.0, + "mPr@90.0": 60.0, + "mIoU": 77.5, + "data_time": 0.0121384064356486, + "time": 0.342536727587382 + }, + "10": { + "mPr@50.0": 84.81, + "mPr@60.0": 80.0, + "mPr@70.0": 72.31, + "mPr@80.0": 63.65, + "mPr@90.0": 24.1, + "mIoU": 70.11, + "data_time": 0.013056959424700056, + "time": 0.029277188437325612 + }, + "11": { + "mPr@50.0": 84.81, + "mPr@60.0": 80.0, + "mPr@70.0": 72.31, + "mPr@80.0": 53.4, + "mPr@90.0": 26.41, + "mIoU": 67.25, + "data_time": 0.015907560076032366, + "time": 0.03202813012259347 + }, + "12": { + "mPr@50.0": 87.68, + "mPr@60.0": 85.89, + "mPr@70.0": 77.86, + "mPr@80.0": 59.7, + "mPr@90.0": 23.81, + "mIoU": 69.95, + "data_time": 0.013392823083060128, + "time": 0.02980405943734305 + }, + "13": { + "mPr@50.0": 82.99, + 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+ ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512' diff --git a/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/show/vis_data/vis_image/test_floor_bottom_seq03_0.png_1.png b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/show/vis_data/vis_image/test_floor_bottom_seq03_0.png_1.png new file mode 100644 index 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b/my_projects/test_results/arid20_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/show/vis_data/vis_image/test_table_top_seq11_9.png_139.png differ diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240918_213205/20240918_213205.json b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240918_213205/20240918_213205.json new file mode 100644 index 0000000000..dba919685b --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240918_213205/20240918_213205.json @@ -0,0 +1 @@ +{"mPr@50.0": 96.6, "mPr@60.0": 96.6, "mPr@70.0": 95.89, "mPr@80.0": 88.92, "mPr@90.0": 49.35, "mIoU": 87.1, "data_time": 0.8139887130517753, "time": 0.9131672828317546} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/benchmark/fps_20240918_213828.json b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/benchmark/fps_20240918_213828.json new file mode 100644 index 0000000000..d34c5824e2 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/benchmark/fps_20240918_213828.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py", + "unit": "img / s", + "overall_fps_1": 14.73, + "overall_memory_1": 584, + "overall_fps_2": 14.6, + "overall_memory_2": 585, + "overall_fps_3": 14.55, + "overall_memory_3": 583, + "overall_fps_4": 14.54, + "overall_memory_4": 582, + "overall_fps_5": 14.52, + "overall_memory_5": 584, + "overall_fps_6": 14.47, + "overall_memory_6": 585, + "overall_fps_7": 14.5, + "overall_memory_7": 582, + "overall_fps_8": 14.49, + "overall_memory_8": 582, + "overall_fps_9": 14.5, + "overall_memory_9": 582, + "overall_fps_10": 14.49, + "overall_memory_10": 581, + "average_fps": 14.54, + "fps_variance": 0.0053, + "average_mem": 583.0, + "mem_variance": 1.8 +} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/20240918_213421/20240918_213421.json b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/20240918_213421/20240918_213421.json new file mode 100644 index 0000000000..ec5a47dc4a --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/20240918_213421/20240918_213421.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 100.0, "mPr@80.0": 100.0, "mPr@90.0": 80.0, "mIoU": 94.91, "data_time": 0.013411283493041992, "time": 0.39904431502024335} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/20240918_213421/vis_data/config.py b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/20240918_213421/vis_data/config.py new file mode 100644 index 0000000000..5d0cd4fa64 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/20240918_213421/vis_data/config.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/9', seg_map_path='ann_dir/9'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9' diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..0a12d600bf --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/1', seg_map_path='ann_dir/1'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1' diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/10/20240918_213427/20240918_213427.json b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/10/20240918_213427/20240918_213427.json new file mode 100644 index 0000000000..e20665d318 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/10/20240918_213427/20240918_213427.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.27, "mPr@60.0": 98.27, "mPr@70.0": 98.27, "mPr@80.0": 95.06, "mPr@90.0": 54.36, "mIoU": 88.99, "data_time": 0.015443427222115653, "time": 0.09218556540352958} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..4bfa862eb1 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/10/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/10', seg_map_path='ann_dir/10'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/10' diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/11/20240918_213439/20240918_213439.json b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/11/20240918_213439/20240918_213439.json new file mode 100644 index 0000000000..1dfac6dbc0 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/11/20240918_213439/20240918_213439.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.27, "mPr@60.0": 98.27, "mPr@70.0": 98.27, "mPr@80.0": 94.1, "mPr@90.0": 58.53, "mIoU": 89.06, "data_time": 0.016953400203159878, "time": 0.0977165699005127} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/11/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/11/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..e74ec75f2f --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/11/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/11', seg_map_path='ann_dir/11'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/11' diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/12/20240918_213452/20240918_213452.json b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/12/20240918_213452/20240918_213452.json new file mode 100644 index 0000000000..9a935c9317 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/12/20240918_213452/20240918_213452.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.39, "mPr@60.0": 98.39, "mPr@70.0": 98.39, "mPr@80.0": 88.27, "mPr@90.0": 57.32, "mIoU": 88.55, "data_time": 0.01618170738220215, "time": 0.09304448536464147} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/12/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/12/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..65efcb5ad7 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/12/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/12', seg_map_path='ann_dir/12'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/12' diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/13/20240918_213504/20240918_213504.json b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/13/20240918_213504/20240918_213504.json new file mode 100644 index 0000000000..2a377036a9 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/13/20240918_213504/20240918_213504.json @@ -0,0 +1 @@ +{"mPr@50.0": 97.93, "mPr@60.0": 97.93, "mPr@70.0": 97.93, "mPr@80.0": 86.44, "mPr@90.0": 48.56, "mIoU": 87.88, "data_time": 0.01704059328351702, "time": 0.09414325441632952} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/13/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/13/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..c7090a56c3 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/13/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/13', seg_map_path='ann_dir/13'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/13' diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/14/20240918_213516/20240918_213516.json b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/14/20240918_213516/20240918_213516.json new file mode 100644 index 0000000000..a216ca8849 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/14/20240918_213516/20240918_213516.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.67, "mPr@60.0": 98.67, "mPr@70.0": 98.0, "mPr@80.0": 88.83, "mPr@90.0": 44.17, "mIoU": 88.0, "data_time": 0.015955448150634766, "time": 0.09590509959629603} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/14/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/14/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..79d044cb56 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/14/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/14', seg_map_path='ann_dir/14'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/14' diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/15/20240918_213529/20240918_213529.json b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/15/20240918_213529/20240918_213529.json new file mode 100644 index 0000000000..53e3abf184 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/15/20240918_213529/20240918_213529.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.67, "mPr@60.0": 98.67, "mPr@70.0": 98.67, "mPr@80.0": 87.17, "mPr@90.0": 49.44, "mIoU": 88.29, "data_time": 0.015317814690726144, "time": 0.09509328433445521} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/15/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/15/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..1d79ca80b3 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/15/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/15', seg_map_path='ann_dir/15'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/15' diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/16/20240918_213541/20240918_213541.json b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/16/20240918_213541/20240918_213541.json new file mode 100644 index 0000000000..579afdac1b --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/16/20240918_213541/20240918_213541.json @@ -0,0 +1 @@ +{"mPr@50.0": 96.44, "mPr@60.0": 96.44, "mPr@70.0": 96.44, "mPr@80.0": 88.0, "mPr@90.0": 50.0, "mIoU": 87.16, "data_time": 0.015253441674368722, "time": 0.09298014640808105} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/16/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/16/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..fba7f1ecc6 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/16/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/16', seg_map_path='ann_dir/16'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/16' diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/17/20240918_213554/20240918_213554.json b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/17/20240918_213554/20240918_213554.json new file mode 100644 index 0000000000..8ff09df2fb --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/17/20240918_213554/20240918_213554.json @@ -0,0 +1 @@ +{"mPr@50.0": 97.29, "mPr@60.0": 97.29, "mPr@70.0": 94.17, "mPr@80.0": 85.57, "mPr@90.0": 45.73, "mIoU": 87.16, "data_time": 0.01601011412484305, "time": 0.09172415733337402} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/17/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/17/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..0fc65141fb --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/17/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/17', seg_map_path='ann_dir/17'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/17' diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/18/20240918_213606/20240918_213606.json b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/18/20240918_213606/20240918_213606.json new file mode 100644 index 0000000000..8f1863fb29 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/18/20240918_213606/20240918_213606.json @@ -0,0 +1 @@ +{"mPr@50.0": 95.73, "mPr@60.0": 95.73, "mPr@70.0": 92.6, "mPr@80.0": 82.45, "mPr@90.0": 41.98, "mIoU": 85.14, "data_time": 0.015286752155848913, "time": 0.0908496720450265} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/18/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/18/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..d83eecbb85 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/18/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/18', seg_map_path='ann_dir/18'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/18' diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/19/20240918_213619/20240918_213619.json b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/19/20240918_213619/20240918_213619.json new file mode 100644 index 0000000000..065aa8e8dc --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/19/20240918_213619/20240918_213619.json @@ -0,0 +1 @@ +{"mPr@50.0": 94.32, "mPr@60.0": 94.32, "mPr@70.0": 91.2, "mPr@80.0": 82.92, "mPr@90.0": 36.67, "mIoU": 84.05, "data_time": 0.015432289668491908, "time": 0.09432172775268555} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/19/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/19/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..b0e981bd21 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/19/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/19', seg_map_path='ann_dir/19'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/19' diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/2/20240918_213631/20240918_213631.json b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/2/20240918_213631/20240918_213631.json new file mode 100644 index 0000000000..3892186b4d --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/2/20240918_213631/20240918_213631.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 100.0, "mPr@80.0": 100.0, "mPr@90.0": 70.0, "mIoU": 93.69, "data_time": 0.01852069582257952, "time": 0.0961836406162807} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/2/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/2/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..d979fe0a13 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/2/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/2', seg_map_path='ann_dir/2'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/2' diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/20/20240918_213643/20240918_213643.json b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/20/20240918_213643/20240918_213643.json new file mode 100644 index 0000000000..118b0d5669 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/20/20240918_213643/20240918_213643.json @@ -0,0 +1 @@ +{"mPr@50.0": 93.8, "mPr@60.0": 93.8, "mPr@70.0": 90.68, "mPr@80.0": 80.05, "mPr@90.0": 38.85, "mIoU": 83.14, "data_time": 0.01554833139692034, "time": 0.09578473227364677} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/20/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/20/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..4f63322a72 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/20/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/20', seg_map_path='ann_dir/20'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/20' diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/3/20240918_213656/20240918_213656.json b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/3/20240918_213656/20240918_213656.json new file mode 100644 index 0000000000..157aa2fe70 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/3/20240918_213656/20240918_213656.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 100.0, "mPr@80.0": 100.0, "mPr@90.0": 61.54, "mIoU": 92.96, "data_time": 0.015804801668439592, "time": 0.0961324828011649} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/3/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/3/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..79c2e35fa0 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/3/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/3', seg_map_path='ann_dir/3'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/3' diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/4/20240918_213708/20240918_213708.json b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/4/20240918_213708/20240918_213708.json new file mode 100644 index 0000000000..e0859e33c0 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/4/20240918_213708/20240918_213708.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 100.0, "mPr@80.0": 96.67, "mPr@90.0": 56.67, "mIoU": 92.25, "data_time": 0.01624161856515067, "time": 0.09518889018467494} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/4/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/4/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..d526547db6 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/4/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/4', seg_map_path='ann_dir/4'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/4' diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/5/20240918_213721/20240918_213721.json b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/5/20240918_213721/20240918_213721.json new file mode 100644 index 0000000000..5b1e706471 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/5/20240918_213721/20240918_213721.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 97.92, "mPr@80.0": 97.92, "mPr@90.0": 59.38, "mIoU": 92.13, "data_time": 0.016741922923496792, "time": 0.09384305136544364} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/5/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/5/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..ee8f562585 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/5/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/5', seg_map_path='ann_dir/5'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/5' diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/6/20240918_213733/20240918_213733.json b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/6/20240918_213733/20240918_213733.json new file mode 100644 index 0000000000..718c9640cb --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/6/20240918_213733/20240918_213733.json @@ -0,0 +1 @@ +{"mPr@50.0": 96.93, "mPr@60.0": 96.93, "mPr@70.0": 96.93, "mPr@80.0": 95.18, "mPr@90.0": 62.72, "mIoU": 88.97, "data_time": 0.02073557036263602, "time": 0.10063290596008301} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/6/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/6/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..84b3695310 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/6/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/6', seg_map_path='ann_dir/6'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/6' diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/7/20240918_213746/20240918_213746.json b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/7/20240918_213746/20240918_213746.json new file mode 100644 index 0000000000..9b62c7df92 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/7/20240918_213746/20240918_213746.json @@ -0,0 +1 @@ +{"mPr@50.0": 97.22, "mPr@60.0": 97.22, "mPr@70.0": 97.22, "mPr@80.0": 95.63, "mPr@90.0": 63.1, "mIoU": 88.67, "data_time": 0.018981286457606723, "time": 0.0968947069985526} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/7/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/7/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..c1491f6ce1 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/7/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/7', seg_map_path='ann_dir/7'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/7' diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/8/20240918_213758/20240918_213758.json b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/8/20240918_213758/20240918_213758.json new file mode 100644 index 0000000000..331654e5ab --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/8/20240918_213758/20240918_213758.json @@ -0,0 +1 @@ +{"mPr@50.0": 97.67, "mPr@60.0": 97.67, "mPr@70.0": 97.67, "mPr@80.0": 96.33, "mPr@90.0": 62.33, "mIoU": 88.63, "data_time": 0.02294370106288365, "time": 0.10000269753592354} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/8/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/8/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..241c85c34f --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/8/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/8', seg_map_path='ann_dir/8'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/8' diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9/20240918_213810/20240918_213810.json b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9/20240918_213810/20240918_213810.json new file mode 100644 index 0000000000..1f001fe8dd --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9/20240918_213810/20240918_213810.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.2, "mPr@60.0": 98.2, "mPr@70.0": 98.2, "mPr@80.0": 97.2, "mPr@90.0": 62.2, "mIoU": 89.09, "data_time": 0.020452499389648438, "time": 0.09853717258998326} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..5d0cd4fa64 --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,432 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/9', seg_map_path='ann_dir/9'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9' diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/clutter_data.json 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a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..68dc0edcec --- /dev/null +++ b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,438 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/mask2former_results.json 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type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git 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b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/show/vis_data/vis_image/test_table_top_seq11_9.png_139.png new file mode 100644 index 0000000000..e612591f06 Binary files /dev/null and b/my_projects/test_results/arid20_cat/mask2former_r50_1xb2-pre-ade20k-10k_arid20_cat-512x512/show/vis_data/vis_image/test_table_top_seq11_9.png_139.png differ diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240918_214159/20240918_214159.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240918_214159/20240918_214159.json new file mode 100644 index 0000000000..70cdfd0c65 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240918_214159/20240918_214159.json @@ -0,0 +1 @@ +{"mPr@50.0": 96.52, "mPr@60.0": 94.51, "mPr@70.0": 92.95, "mPr@80.0": 85.7, "mPr@90.0": 49.72, "mIoU": 85.94, "data_time": 0.7972581163584758, "time": 0.8737508547391822} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/benchmark/fps_20240918_214903.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/benchmark/fps_20240918_214903.json new file mode 100644 index 0000000000..ecad57bf94 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/benchmark/fps_20240918_214903.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py", + "unit": "img / s", + "overall_fps_1": 21.7, + "overall_memory_1": 551, + "overall_fps_2": 21.57, + "overall_memory_2": 552, + "overall_fps_3": 21.37, + "overall_memory_3": 551, + "overall_fps_4": 21.57, + "overall_memory_4": 551, + "overall_fps_5": 22.17, + "overall_memory_5": 551, + "overall_fps_6": 21.66, + "overall_memory_6": 551, + "overall_fps_7": 21.52, + "overall_memory_7": 551, + "overall_fps_8": 22.97, + "overall_memory_8": 551, + "overall_fps_9": 22.89, + "overall_memory_9": 551, + "overall_fps_10": 23.07, + "overall_memory_10": 551, + "average_fps": 22.05, + "fps_variance": 0.409, + "average_mem": 551.1, + "mem_variance": 0.09 +} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/20240918_214412/20240918_214412.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/20240918_214412/20240918_214412.json new file mode 100644 index 0000000000..386880083d --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/20240918_214412/20240918_214412.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 100.0, "mPr@80.0": 100.0, "mPr@90.0": 80.0, "mIoU": 95.39, "data_time": 0.013628880182902018, "time": 0.3480665683746338} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/20240918_214412/vis_data/config.py b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/20240918_214412/vis_data/config.py new file mode 100644 index 0000000000..41ef3b01f0 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/20240918_214412/vis_data/config.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/9', seg_map_path='ann_dir/9'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..d8d0da19ae --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/1', seg_map_path='ann_dir/1'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/10/20240918_214420/20240918_214420.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/10/20240918_214420/20240918_214420.json new file mode 100644 index 0000000000..675f24e29a --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/10/20240918_214420/20240918_214420.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.27, "mPr@60.0": 98.27, "mPr@70.0": 98.27, "mPr@80.0": 95.06, "mPr@90.0": 66.54, "mIoU": 89.36, "data_time": 0.015918970108032227, "time": 0.06807099069867815} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..78f8af51a5 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/10/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/10', seg_map_path='ann_dir/10'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/10' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/11/20240918_214435/20240918_214435.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/11/20240918_214435/20240918_214435.json new file mode 100644 index 0000000000..89aab6dc2d --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/11/20240918_214435/20240918_214435.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.27, "mPr@60.0": 98.27, "mPr@70.0": 98.27, "mPr@80.0": 91.22, "mPr@90.0": 65.58, "mIoU": 88.84, "data_time": 0.015436853681291853, "time": 0.06978389195033483} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/11/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/11/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..50e660b867 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/11/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/11', seg_map_path='ann_dir/11'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/11' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/12/20240918_214450/20240918_214450.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/12/20240918_214450/20240918_214450.json new file mode 100644 index 0000000000..cea5ceaef7 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/12/20240918_214450/20240918_214450.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.39, "mPr@60.0": 98.39, "mPr@70.0": 98.39, "mPr@80.0": 89.46, "mPr@90.0": 64.76, "mIoU": 88.62, "data_time": 0.015832832881382534, "time": 0.06943535804748535} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/12/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/12/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..d601874158 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/12/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/12', seg_map_path='ann_dir/12'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/12' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/13/20240918_214504/20240918_214504.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/13/20240918_214504/20240918_214504.json new file mode 100644 index 0000000000..e40eeb6e8e --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/13/20240918_214504/20240918_214504.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.62, "mPr@60.0": 98.62, "mPr@70.0": 98.62, "mPr@80.0": 91.61, "mPr@90.0": 56.61, "mIoU": 88.73, "data_time": 0.01611804962158203, "time": 0.06914029802594866} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/13/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/13/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..9f75b00dcc --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/13/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/13', seg_map_path='ann_dir/13'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/13' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/14/20240918_214519/20240918_214519.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/14/20240918_214519/20240918_214519.json new file mode 100644 index 0000000000..4e840b4817 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/14/20240918_214519/20240918_214519.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.67, "mPr@60.0": 98.67, "mPr@70.0": 98.67, "mPr@80.0": 91.72, "mPr@90.0": 49.61, "mIoU": 88.38, "data_time": 0.01595306396484375, "time": 0.07024812698364258} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/14/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/14/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..80fa733e04 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/14/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/14', seg_map_path='ann_dir/14'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/14' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/15/20240918_214534/20240918_214534.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/15/20240918_214534/20240918_214534.json new file mode 100644 index 0000000000..13352e63db --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/15/20240918_214534/20240918_214534.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.67, "mPr@60.0": 98.67, "mPr@70.0": 98.67, "mPr@80.0": 92.17, "mPr@90.0": 53.61, "mIoU": 88.65, "data_time": 0.01577728135245187, "time": 0.06900460379464286} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/15/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/15/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..ecfed17fcb --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/15/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/15', seg_map_path='ann_dir/15'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/15' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/16/20240918_214548/20240918_214548.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/16/20240918_214548/20240918_214548.json new file mode 100644 index 0000000000..dabdc0d255 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/16/20240918_214548/20240918_214548.json @@ -0,0 +1 @@ +{"mPr@50.0": 97.0, "mPr@60.0": 93.67, "mPr@70.0": 90.61, "mPr@80.0": 86.44, "mPr@90.0": 49.17, "mIoU": 85.94, "data_time": 0.015494755336216517, "time": 0.06650924682617188} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/16/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/16/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..83f79efd7a --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/16/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/16', seg_map_path='ann_dir/16'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/16' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/17/20240918_214603/20240918_214603.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/17/20240918_214603/20240918_214603.json new file mode 100644 index 0000000000..6bc3e8ee78 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/17/20240918_214603/20240918_214603.json @@ -0,0 +1 @@ +{"mPr@50.0": 94.95, "mPr@60.0": 91.82, "mPr@70.0": 90.52, "mPr@80.0": 80.36, "mPr@90.0": 42.34, "mIoU": 84.43, "data_time": 0.01611614227294922, "time": 0.06696462631225586} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/17/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/17/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..ecafc1d365 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/17/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/17', seg_map_path='ann_dir/17'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/17' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/18/20240918_214617/20240918_214617.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/18/20240918_214617/20240918_214617.json new file mode 100644 index 0000000000..32416212ff --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/18/20240918_214617/20240918_214617.json @@ -0,0 +1 @@ +{"mPr@50.0": 95.73, "mPr@60.0": 92.6, "mPr@70.0": 88.18, "mPr@80.0": 78.28, "mPr@90.0": 39.48, "mIoU": 83.42, "data_time": 0.015749147960117886, "time": 0.0671901021684919} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/18/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/18/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..db3abb0619 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/18/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/18', seg_map_path='ann_dir/18'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/18' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/19/20240918_214632/20240918_214632.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/19/20240918_214632/20240918_214632.json new file mode 100644 index 0000000000..0afdc4860a --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/19/20240918_214632/20240918_214632.json @@ -0,0 +1 @@ +{"mPr@50.0": 93.39, "mPr@60.0": 90.26, "mPr@70.0": 85.31, "mPr@80.0": 73.54, "mPr@90.0": 35.89, "mIoU": 81.62, "data_time": 0.015787158693586077, "time": 0.06755893571036202} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/19/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/19/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..515792a708 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/19/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/19', seg_map_path='ann_dir/19'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/19' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/2/20240918_214647/20240918_214647.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/2/20240918_214647/20240918_214647.json new file mode 100644 index 0000000000..e6df17f34c --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/2/20240918_214647/20240918_214647.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 100.0, "mPr@80.0": 100.0, "mPr@90.0": 75.0, "mIoU": 94.17, "data_time": 0.019170148032052175, "time": 0.07552388736179896} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/2/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/2/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..032b26cdf7 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/2/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/2', seg_map_path='ann_dir/2'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/2' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/20/20240918_214701/20240918_214701.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/20/20240918_214701/20240918_214701.json new file mode 100644 index 0000000000..e4585af02e --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/20/20240918_214701/20240918_214701.json @@ -0,0 +1 @@ +{"mPr@50.0": 93.8, "mPr@60.0": 89.9, "mPr@70.0": 88.23, "mPr@80.0": 73.49, "mPr@90.0": 31.51, "mIoU": 81.84, "data_time": 0.016023056847708567, "time": 0.07010773250034877} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/20/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/20/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..ad22a86046 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/20/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/20', seg_map_path='ann_dir/20'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/20' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/3/20240918_214716/20240918_214716.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/3/20240918_214716/20240918_214716.json new file mode 100644 index 0000000000..cf146e3828 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/3/20240918_214716/20240918_214716.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 100.0, "mPr@80.0": 100.0, "mPr@90.0": 80.77, "mIoU": 93.84, "data_time": 0.0215087958744594, "time": 0.07625978333609444} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/3/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/3/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..0339c41272 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/3/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/3', seg_map_path='ann_dir/3'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/3' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/4/20240918_214731/20240918_214731.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/4/20240918_214731/20240918_214731.json new file mode 100644 index 0000000000..d836c71f79 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/4/20240918_214731/20240918_214731.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 100.0, "mPr@80.0": 100.0, "mPr@90.0": 66.67, "mIoU": 93.14, "data_time": 0.01617898259844099, "time": 0.06933992249625069} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/4/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/4/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..33190b2968 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/4/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/4', seg_map_path='ann_dir/4'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/4' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/5/20240918_214745/20240918_214745.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/5/20240918_214745/20240918_214745.json new file mode 100644 index 0000000000..f5c7cbbb3d --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/5/20240918_214745/20240918_214745.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 100.0, "mPr@80.0": 100.0, "mPr@90.0": 60.42, "mIoU": 92.85, "data_time": 0.017817940030779158, "time": 0.07127955981663295} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/5/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/5/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..b7c940e643 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/5/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/5', seg_map_path='ann_dir/5'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/5' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/6/20240918_214800/20240918_214800.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/6/20240918_214800/20240918_214800.json new file mode 100644 index 0000000000..a51bb3563b --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/6/20240918_214800/20240918_214800.json @@ -0,0 +1 @@ +{"mPr@50.0": 96.93, "mPr@60.0": 96.93, "mPr@70.0": 96.93, "mPr@80.0": 96.93, "mPr@90.0": 56.58, "mIoU": 89.16, "data_time": 0.0212768486567906, "time": 0.07388295446123395} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/6/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/6/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..3172c2eca8 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/6/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/6', seg_map_path='ann_dir/6'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/6' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/7/20240918_214815/20240918_214815.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/7/20240918_214815/20240918_214815.json new file mode 100644 index 0000000000..abfb9a9209 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/7/20240918_214815/20240918_214815.json @@ -0,0 +1 @@ +{"mPr@50.0": 97.22, "mPr@60.0": 97.22, "mPr@70.0": 97.22, "mPr@80.0": 97.22, "mPr@90.0": 70.24, "mIoU": 89.24, "data_time": 0.02299598285130092, "time": 0.07884063039507185} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/7/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/7/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..e8df905f33 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/7/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/7', seg_map_path='ann_dir/7'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/7' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/8/20240918_214829/20240918_214829.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/8/20240918_214829/20240918_214829.json new file mode 100644 index 0000000000..1fc633e090 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/8/20240918_214829/20240918_214829.json @@ -0,0 +1 @@ +{"mPr@50.0": 97.67, "mPr@60.0": 97.67, "mPr@70.0": 97.67, "mPr@80.0": 97.67, "mPr@90.0": 71.0, "mIoU": 89.38, "data_time": 0.018472535269601003, "time": 0.07244712965829032} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/8/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/8/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..77bc39b282 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/8/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/8', seg_map_path='ann_dir/8'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/8' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9/20240918_214844/20240918_214844.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9/20240918_214844/20240918_214844.json new file mode 100644 index 0000000000..0d88580d17 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9/20240918_214844/20240918_214844.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.2, "mPr@60.0": 98.2, "mPr@70.0": 98.2, "mPr@80.0": 98.2, "mPr@90.0": 72.2, "mIoU": 89.74, "data_time": 0.019322974341256276, "time": 0.07370080266680036} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..41ef3b01f0 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,522 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/9', seg_map_path='ann_dir/9'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/clutter_data.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/clutter_data.json new file mode 100644 index 0000000000..a594941f2c --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/clutter_data.json @@ -0,0 +1,202 @@ +{ + "1": { + "mPr@50.0": 100.0, + "mPr@60.0": 100.0, + "mPr@70.0": 100.0, + "mPr@80.0": 100.0, + "mPr@90.0": 80.0, + "mIoU": 95.39, + "data_time": 0.013628880182902018, + "time": 0.3480665683746338 + }, + "10": { + "mPr@50.0": 98.27, + "mPr@60.0": 98.27, + "mPr@70.0": 98.27, + "mPr@80.0": 95.06, + "mPr@90.0": 66.54, + "mIoU": 89.36, + "data_time": 0.015918970108032227, + "time": 0.06807099069867815 + }, + "11": { + "mPr@50.0": 98.27, + "mPr@60.0": 98.27, + "mPr@70.0": 98.27, + "mPr@80.0": 91.22, + "mPr@90.0": 65.58, + "mIoU": 88.84, + "data_time": 0.015436853681291853, + "time": 0.06978389195033483 + }, + "12": { + "mPr@50.0": 98.39, + "mPr@60.0": 98.39, + "mPr@70.0": 98.39, + "mPr@80.0": 89.46, + "mPr@90.0": 64.76, + "mIoU": 88.62, + "data_time": 0.015832832881382534, + "time": 0.06943535804748535 + }, + "13": { + "mPr@50.0": 98.62, + 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a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/confusion_matrix/top_10_confusion.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..a3e53af79d --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "marker", + "pred_label": "kleenex", + "score": 33.94241935763437 + }, + { + "gt_label": "lime", + "pred_label": "apple", + "score": 11.622116947335662 + }, + { + "gt_label": "potato", + "pred_label": "lemon", + "score": 11.4434052843071 + }, + { + "gt_label": "sponge", + "pred_label": "kleenex", + "score": 8.31716466175139 + }, + { + "gt_label": "sponge", + "pred_label": "cereal_box", + "score": 7.825637045660329 + }, + { + "gt_label": "ball", + "pred_label": 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a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_results.json b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_results.json new file mode 100644 index 0000000000..5a0371960c --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50.0": 96.52, + "mPr@60.0": 94.51, + "mPr@70.0": 92.95, + "mPr@80.0": 85.7, + "mPr@90.0": 49.72, + "mIoU": 85.94, + "average_fps": 22.05, + "fps_variance": 0.409, + "average_mem": 551.1, + "mem_variance": 0.09 +} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..522c162821 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,528 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/pred_results/floor_bottom_seq03_0.png b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/pred_results/floor_bottom_seq03_0.png new file mode 100644 index 0000000000..dc3661269b Binary files /dev/null and b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/pred_results/floor_bottom_seq03_0.png differ diff --git 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100644 index 0000000000..522c162821 --- /dev/null +++ b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/show/vis_data/config.py @@ -0,0 +1,528 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=32, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 32 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/show/vis_data/vis_image/test_floor_bottom_seq03_0.png_1.png b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/show/vis_data/vis_image/test_floor_bottom_seq03_0.png_1.png new file mode 100644 index 0000000000..cd0f57ee0d Binary files /dev/null and b/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/show/vis_data/vis_image/test_floor_bottom_seq03_0.png_1.png differ diff --git a/my_projects/test_results/arid20_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_arid20_cat-512x512/show/vis_data/vis_image/test_floor_bottom_seq03_1.png_2.png 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b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/20240918_215151/20240918_215151.json @@ -0,0 +1 @@ +{"mPr@50.0": 97.57, "mPr@60.0": 96.54, "mPr@70.0": 92.88, "mPr@80.0": 73.58, "mPr@90.0": 28.07, "mIoU": 83.22, "data_time": 0.7848378462757138, "time": 0.8284120868435867} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/benchmark/fps_20240918_215746.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/benchmark/fps_20240918_215746.json new file mode 100644 index 0000000000..8a1bb2eb7a --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/benchmark/fps_20240918_215746.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py", + "unit": "img / s", + "overall_fps_1": 63.57, + "overall_memory_1": 355, + "overall_fps_2": 63.43, + "overall_memory_2": 355, + "overall_fps_3": 63.68, + "overall_memory_3": 356, + "overall_fps_4": 63.57, + "overall_memory_4": 355, + "overall_fps_5": 63.42, + "overall_memory_5": 355, + "overall_fps_6": 63.74, + "overall_memory_6": 354, + "overall_fps_7": 62.45, + "overall_memory_7": 354, + "overall_fps_8": 62.89, + "overall_memory_8": 354, + "overall_fps_9": 62.63, + "overall_memory_9": 354, + "overall_fps_10": 65.3, + "overall_memory_10": 354, + "average_fps": 63.47, + "fps_variance": 0.5552, + "average_mem": 354.6, + "mem_variance": 0.44 +} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/1/20240918_215354/20240918_215354.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/1/20240918_215354/20240918_215354.json new file mode 100644 index 0000000000..6b5ab8eb30 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/1/20240918_215354/20240918_215354.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 100.0, "mPr@80.0": 100.0, "mPr@90.0": 93.33, "mIoU": 78.76, "data_time": 0.01584450403849284, "time": 0.3578728437423706} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/1/20240918_215354/vis_data/config.py b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/1/20240918_215354/vis_data/config.py new file mode 100644 index 0000000000..9ec2706641 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/1/20240918_215354/vis_data/config.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/9', seg_map_path='ann_dir/9'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/9' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/1/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/1/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..3c82b6a2e6 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/1/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/1', seg_map_path='ann_dir/1'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/1' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/10/20240918_215400/20240918_215400.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/10/20240918_215400/20240918_215400.json new file mode 100644 index 0000000000..73f7402c24 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/10/20240918_215400/20240918_215400.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.27, "mPr@60.0": 98.27, "mPr@70.0": 98.27, "mPr@80.0": 80.32, "mPr@90.0": 36.28, "mIoU": 85.28, "data_time": 0.01554312024797712, "time": 0.0376702036176409} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..fe9f4e4735 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/10/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/10', seg_map_path='ann_dir/10'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/10' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/11/20240918_215412/20240918_215412.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/11/20240918_215412/20240918_215412.json new file mode 100644 index 0000000000..e778a73249 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/11/20240918_215412/20240918_215412.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.27, "mPr@60.0": 98.27, "mPr@70.0": 97.63, "mPr@80.0": 75.83, "mPr@90.0": 34.87, "mIoU": 84.51, "data_time": 0.015220063073294503, "time": 0.03551718166896275} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/11/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/11/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..6191156259 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/11/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/11', seg_map_path='ann_dir/11'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/11' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/12/20240918_215423/20240918_215423.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/12/20240918_215423/20240918_215423.json new file mode 100644 index 0000000000..7503076370 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/12/20240918_215423/20240918_215423.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.39, "mPr@60.0": 98.39, "mPr@70.0": 97.8, "mPr@80.0": 87.38, "mPr@90.0": 30.95, "mIoU": 85.11, "data_time": 0.014487607138497489, "time": 0.03402107102530343} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/12/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/12/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..b9d1fcf4a1 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/12/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/12', seg_map_path='ann_dir/12'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/12' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/13/20240918_215435/20240918_215435.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/13/20240918_215435/20240918_215435.json new file mode 100644 index 0000000000..9f8bc328ef --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/13/20240918_215435/20240918_215435.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.62, "mPr@60.0": 98.62, "mPr@70.0": 97.36, "mPr@80.0": 80.57, "mPr@90.0": 26.44, "mIoU": 84.78, "data_time": 0.014904124396187919, "time": 0.034451110022408624} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/13/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/13/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..a5caf5bbbd --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/13/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/13', seg_map_path='ann_dir/13'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/13' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/14/20240918_215447/20240918_215447.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/14/20240918_215447/20240918_215447.json new file mode 100644 index 0000000000..bd5c26e3d9 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/14/20240918_215447/20240918_215447.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.67, "mPr@60.0": 98.67, "mPr@70.0": 97.44, "mPr@80.0": 79.56, "mPr@90.0": 24.94, "mIoU": 84.34, "data_time": 0.01491696493966239, "time": 0.036625180925641744} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/14/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/14/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..a158cfa965 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/14/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/14', seg_map_path='ann_dir/14'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/14' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/15/20240918_215458/20240918_215458.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/15/20240918_215458/20240918_215458.json new file mode 100644 index 0000000000..58dc83745b --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/15/20240918_215458/20240918_215458.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.67, "mPr@60.0": 98.67, "mPr@70.0": 96.06, "mPr@80.0": 79.28, "mPr@90.0": 23.83, "mIoU": 81.06, "data_time": 0.01827965463910784, "time": 0.04096497808183942} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/15/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/15/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..eda7eed92b --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/15/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/15', seg_map_path='ann_dir/15'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/15' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/16/20240918_215510/20240918_215510.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/16/20240918_215510/20240918_215510.json new file mode 100644 index 0000000000..85773d8ac3 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/16/20240918_215510/20240918_215510.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.67, "mPr@60.0": 98.67, "mPr@70.0": 94.94, "mPr@80.0": 73.17, "mPr@90.0": 20.94, "mIoU": 80.41, "data_time": 0.015035629272460938, "time": 0.0363572325025286} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/16/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/16/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..1a0ed77b8b --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/16/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/16', seg_map_path='ann_dir/16'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/16' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/17/20240918_215522/20240918_215522.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/17/20240918_215522/20240918_215522.json new file mode 100644 index 0000000000..e21f314ddd --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/17/20240918_215522/20240918_215522.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.33, "mPr@60.0": 97.03, "mPr@70.0": 91.04, "mPr@80.0": 68.65, "mPr@90.0": 18.85, "mIoU": 81.8, "data_time": 0.016029187611171176, "time": 0.03519620214189802} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/17/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/17/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..3fccd8aa50 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/17/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/17', seg_map_path='ann_dir/17'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/17' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/18/20240918_215533/20240918_215533.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/18/20240918_215533/20240918_215533.json new file mode 100644 index 0000000000..dd06deb9da --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/18/20240918_215533/20240918_215533.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.33, "mPr@60.0": 95.47, "mPr@70.0": 88.96, "mPr@80.0": 63.85, "mPr@90.0": 17.03, "mIoU": 80.6, "data_time": 0.014662436076572962, "time": 0.03509971073695591} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/18/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/18/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..be00b3a7c9 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/18/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/18', seg_map_path='ann_dir/18'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/18' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/19/20240918_215545/20240918_215545.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/19/20240918_215545/20240918_215545.json new file mode 100644 index 0000000000..b4c1b169dc --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/19/20240918_215545/20240918_215545.json @@ -0,0 +1 @@ +{"mPr@50.0": 96.3, "mPr@60.0": 92.4, "mPr@70.0": 86.04, "mPr@80.0": 58.7, "mPr@90.0": 15.47, "mIoU": 79.46, "data_time": 0.019962753568376814, "time": 0.04237113680158343} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/19/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/19/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..a0333ede58 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/19/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/19', seg_map_path='ann_dir/19'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/19' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/2/20240918_215557/20240918_215557.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/2/20240918_215557/20240918_215557.json new file mode 100644 index 0000000000..37599ba055 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/2/20240918_215557/20240918_215557.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 100.0, "mPr@80.0": 100.0, "mPr@90.0": 71.67, "mIoU": 92.94, "data_time": 0.02726820537022182, "time": 0.05031500543866839} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/2/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/2/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..bd19cce7b7 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/2/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/2', seg_map_path='ann_dir/2'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/2' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/20/20240918_215608/20240918_215608.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/20/20240918_215608/20240918_215608.json new file mode 100644 index 0000000000..6426ec2f68 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/20/20240918_215608/20240918_215608.json @@ -0,0 +1 @@ +{"mPr@50.0": 95.26, "mPr@60.0": 93.07, "mPr@70.0": 82.86, "mPr@80.0": 55.16, "mPr@90.0": 13.12, "mIoU": 78.16, "data_time": 0.0180750914982387, "time": 0.03957966395786831} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/20/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/20/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..7a296cc9ff --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/20/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/20', seg_map_path='ann_dir/20'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/20' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/3/20240918_215620/20240918_215620.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/3/20240918_215620/20240918_215620.json new file mode 100644 index 0000000000..f0a4b4764d --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/3/20240918_215620/20240918_215620.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 96.15, "mPr@80.0": 96.15, "mPr@90.0": 75.0, "mIoU": 92.13, "data_time": 0.015344790049961634, "time": 0.03717994689941406} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/3/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/3/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..3a8162a788 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/3/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/3', seg_map_path='ann_dir/3'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/3' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/4/20240918_215632/20240918_215632.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/4/20240918_215632/20240918_215632.json new file mode 100644 index 0000000000..fac9b9b808 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/4/20240918_215632/20240918_215632.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 100.0, "mPr@80.0": 96.67, "mPr@90.0": 61.67, "mIoU": 91.52, "data_time": 0.018833364759172713, "time": 0.0404092584337507} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/4/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/4/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..3c7f4515b7 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/4/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/4', seg_map_path='ann_dir/4'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/4' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/5/20240918_215643/20240918_215643.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/5/20240918_215643/20240918_215643.json new file mode 100644 index 0000000000..5e9d844b96 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/5/20240918_215643/20240918_215643.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 100.0, "mPr@80.0": 95.83, "mPr@90.0": 62.5, "mIoU": 80.78, "data_time": 0.01890928404671805, "time": 0.03987629073006766} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/5/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/5/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..019959e2a7 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/5/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/5', seg_map_path='ann_dir/5'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/5' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/6/20240918_215655/20240918_215655.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/6/20240918_215655/20240918_215655.json new file mode 100644 index 0000000000..019dbd1766 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/6/20240918_215655/20240918_215655.json @@ -0,0 +1 @@ +{"mPr@50.0": 96.93, "mPr@60.0": 96.93, "mPr@70.0": 96.93, "mPr@80.0": 92.54, "mPr@90.0": 59.21, "mIoU": 87.25, "data_time": 0.022131034306117466, "time": 0.044919490814208984} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/6/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/6/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..429f3daf82 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/6/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/6', seg_map_path='ann_dir/6'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/6' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/7/20240918_215707/20240918_215707.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/7/20240918_215707/20240918_215707.json new file mode 100644 index 0000000000..9e2869709d --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/7/20240918_215707/20240918_215707.json @@ -0,0 +1 @@ +{"mPr@50.0": 97.22, "mPr@60.0": 97.22, "mPr@70.0": 97.22, "mPr@80.0": 90.87, "mPr@90.0": 48.81, "mIoU": 82.62, "data_time": 0.026577881404331753, "time": 0.04987713268824986} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/7/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/7/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..0e855771d2 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/7/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/7', seg_map_path='ann_dir/7'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/7' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/8/20240918_215719/20240918_215719.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/8/20240918_215719/20240918_215719.json new file mode 100644 index 0000000000..85e182c9c4 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/8/20240918_215719/20240918_215719.json @@ -0,0 +1 @@ +{"mPr@50.0": 97.67, "mPr@60.0": 93.67, "mPr@70.0": 91.67, "mPr@80.0": 86.33, "mPr@90.0": 49.33, "mIoU": 82.02, "data_time": 0.017066240310668945, "time": 0.03858004297528948} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/8/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/8/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..6026ccee7f --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/8/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/8', seg_map_path='ann_dir/8'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/8' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/9/20240918_215730/20240918_215730.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/9/20240918_215730/20240918_215730.json new file mode 100644 index 0000000000..57626de18d --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/9/20240918_215730/20240918_215730.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.2, "mPr@60.0": 98.2, "mPr@70.0": 98.2, "mPr@80.0": 92.87, "mPr@90.0": 45.73, "mIoU": 83.29, "data_time": 0.02041472707475935, "time": 0.041887453624180386} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/9/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/9/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..9ec2706641 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/9/segformer_mit-b0_1xb2-10k_arid20_cat-512x512.py @@ -0,0 +1,319 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/9', seg_map_path='ann_dir/9'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/9' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/clutter_data.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/clutter_data.json new file mode 100644 index 0000000000..78bbb7f1d8 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/clutter/clutter_data.json @@ -0,0 +1,202 @@ +{ + "1": { + "mPr@50.0": 100.0, + "mPr@60.0": 100.0, + "mPr@70.0": 100.0, + "mPr@80.0": 100.0, + "mPr@90.0": 93.33, + "mIoU": 78.76, + "data_time": 0.01584450403849284, + "time": 0.3578728437423706 + }, + "10": { + "mPr@50.0": 98.27, + "mPr@60.0": 98.27, + "mPr@70.0": 98.27, + "mPr@80.0": 80.32, + "mPr@90.0": 36.28, + "mIoU": 85.28, + "data_time": 0.01554312024797712, + "time": 0.0376702036176409 + }, + "11": { + "mPr@50.0": 98.27, + "mPr@60.0": 98.27, + "mPr@70.0": 97.63, + "mPr@80.0": 75.83, + "mPr@90.0": 34.87, + "mIoU": 84.51, + "data_time": 0.015220063073294503, + "time": 0.03551718166896275 + }, + "12": { + "mPr@50.0": 98.39, + "mPr@60.0": 98.39, + "mPr@70.0": 97.8, + "mPr@80.0": 87.38, + "mPr@90.0": 30.95, + "mIoU": 85.11, + "data_time": 0.014487607138497489, + "time": 0.03402107102530343 + }, + "13": { + "mPr@50.0": 98.62, + "mPr@60.0": 98.62, + "mPr@70.0": 97.36, + "mPr@80.0": 80.57, + "mPr@90.0": 26.44, + "mIoU": 84.78, + "data_time": 0.014904124396187919, + "time": 0.034451110022408624 + }, + "14": { + "mPr@50.0": 98.67, + "mPr@60.0": 98.67, + "mPr@70.0": 97.44, + "mPr@80.0": 79.56, + "mPr@90.0": 24.94, + "mIoU": 84.34, + "data_time": 0.01491696493966239, + "time": 0.036625180925641744 + }, + "15": { + "mPr@50.0": 98.67, + "mPr@60.0": 98.67, + "mPr@70.0": 96.06, + "mPr@80.0": 79.28, + "mPr@90.0": 23.83, + "mIoU": 81.06, + "data_time": 0.01827965463910784, + "time": 0.04096497808183942 + }, + "16": { + "mPr@50.0": 98.67, + "mPr@60.0": 98.67, + "mPr@70.0": 94.94, + "mPr@80.0": 73.17, + "mPr@90.0": 20.94, + "mIoU": 80.41, + "data_time": 0.015035629272460938, + "time": 0.0363572325025286 + }, + "17": { + "mPr@50.0": 98.33, + "mPr@60.0": 97.03, + "mPr@70.0": 91.04, + "mPr@80.0": 68.65, + "mPr@90.0": 18.85, + "mIoU": 81.8, + "data_time": 0.016029187611171176, + "time": 0.03519620214189802 + }, + "18": { + "mPr@50.0": 98.33, + "mPr@60.0": 95.47, + "mPr@70.0": 88.96, + "mPr@80.0": 63.85, + "mPr@90.0": 17.03, + "mIoU": 80.6, + "data_time": 0.014662436076572962, + "time": 0.03509971073695591 + }, + "19": { + "mPr@50.0": 96.3, + "mPr@60.0": 92.4, + "mPr@70.0": 86.04, + "mPr@80.0": 58.7, + "mPr@90.0": 15.47, + "mIoU": 79.46, + "data_time": 0.019962753568376814, + "time": 0.04237113680158343 + }, + "2": { + "mPr@50.0": 100.0, + "mPr@60.0": 100.0, + "mPr@70.0": 100.0, + "mPr@80.0": 100.0, + "mPr@90.0": 71.67, + "mIoU": 92.94, + "data_time": 0.02726820537022182, + "time": 0.05031500543866839 + }, + "20": { + "mPr@50.0": 95.26, + 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'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/segformer_results.json b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/segformer_results.json new file mode 100644 index 0000000000..a3ab4a1d36 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/segformer_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50.0": 97.57, + "mPr@60.0": 96.54, + "mPr@70.0": 92.88, + "mPr@80.0": 73.58, + "mPr@90.0": 28.07, + "mIoU": 83.22, + "average_fps": 63.47, + "fps_variance": 0.5552, + "average_mem": 354.6, + "mem_variance": 0.44 +} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/show/vis_data/config.py b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..8b4dc32d86 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/show/vis_data/config.py @@ -0,0 +1,325 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512' diff --git a/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/show/vis_data/vis_image/test_floor_bottom_seq03_0.png_1.png b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/show/vis_data/vis_image/test_floor_bottom_seq03_0.png_1.png new file mode 100644 index 0000000000..35756d9e20 Binary files /dev/null and b/my_projects/test_results/arid20_cat/segformer_mit-b0_1xb2-10k_arid20_cat-512x512/show/vis_data/vis_image/test_floor_bottom_seq03_0.png_1.png differ diff --git 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mode 100644 index 0000000000..1061daf593 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/20240918_215931/20240918_215931.json @@ -0,0 +1 @@ +{"mPr@50.0": 97.22, "mPr@60.0": 95.79, "mPr@70.0": 91.39, "mPr@80.0": 77.1, "mPr@90.0": 35.74, "mIoU": 84.36, "data_time": 0.8216028539396876, "time": 0.8641322379489597} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/benchmark/fps_20240918_220547.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/benchmark/fps_20240918_220547.json new file mode 100644 index 0000000000..ff3538bab9 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/benchmark/fps_20240918_220547.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py", + "unit": "img / s", + "overall_fps_1": 52.65, + "overall_memory_1": 191, + "overall_fps_2": 52.26, + "overall_memory_2": 192, + "overall_fps_3": 51.81, + "overall_memory_3": 192, + "overall_fps_4": 51.64, + "overall_memory_4": 192, + "overall_fps_5": 52.15, + "overall_memory_5": 192, + "overall_fps_6": 54.73, + "overall_memory_6": 192, + "overall_fps_7": 56.31, + "overall_memory_7": 192, + "overall_fps_8": 56.68, + "overall_memory_8": 192, + "overall_fps_9": 56.34, + "overall_memory_9": 192, + "overall_fps_10": 56.67, + "overall_memory_10": 192, + "average_fps": 54.12, + "fps_variance": 4.4193, + "average_mem": 191.9, + "mem_variance": 0.09 +} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/20240918_220140/20240918_220140.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/20240918_220140/20240918_220140.json new file mode 100644 index 0000000000..cbf4ad3b26 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/20240918_220140/20240918_220140.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 100.0, "mPr@80.0": 100.0, "mPr@90.0": 80.0, "mIoU": 94.96, "data_time": 0.013198018074035645, "time": 0.37810003757476807} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/20240918_220140/vis_data/config.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/20240918_220140/vis_data/config.py new file mode 100644 index 0000000000..f871598c94 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/20240918_220140/vis_data/config.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/9', seg_map_path='ann_dir/9'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..7b682f5418 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/1', seg_map_path='ann_dir/1'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/1' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/10/20240918_220146/20240918_220146.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/10/20240918_220146/20240918_220146.json new file mode 100644 index 0000000000..a808f1e32a --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/10/20240918_220146/20240918_220146.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.27, "mPr@60.0": 98.27, "mPr@70.0": 98.27, "mPr@80.0": 89.94, "mPr@90.0": 43.53, "mIoU": 86.87, "data_time": 0.01602271624973842, "time": 0.042276246207101006} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..7ff272fef1 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/10/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/10', seg_map_path='ann_dir/10'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/10' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/11/20240918_220159/20240918_220159.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/11/20240918_220159/20240918_220159.json new file mode 100644 index 0000000000..5c8c3a28af --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/11/20240918_220159/20240918_220159.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.27, "mPr@60.0": 98.27, "mPr@70.0": 98.27, "mPr@80.0": 87.05, "mPr@90.0": 50.38, "mIoU": 86.95, "data_time": 0.015964950834001814, "time": 0.04163731847490583} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/11/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/11/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..6715ea98fd --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/11/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/11', seg_map_path='ann_dir/11'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/11' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/12/20240918_220211/20240918_220211.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/12/20240918_220211/20240918_220211.json new file mode 100644 index 0000000000..6f68892144 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/12/20240918_220211/20240918_220211.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.39, "mPr@60.0": 98.39, "mPr@70.0": 98.39, "mPr@80.0": 81.01, "mPr@90.0": 40.12, "mIoU": 86.08, "data_time": 0.01577295575823103, "time": 0.04078766277858189} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/12/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/12/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..55d1354538 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/12/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/12', seg_map_path='ann_dir/12'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/12' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/13/20240918_220224/20240918_220224.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/13/20240918_220224/20240918_220224.json new file mode 100644 index 0000000000..398b09dbfc --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/13/20240918_220224/20240918_220224.json @@ -0,0 +1 @@ +{"mPr@50.0": 96.9, "mPr@60.0": 95.17, "mPr@70.0": 91.9, "mPr@80.0": 71.67, "mPr@90.0": 37.99, "mIoU": 83.96, "data_time": 0.01549393790108817, "time": 0.04129191807338169} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/13/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/13/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..37a50c96c9 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/13/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/13', seg_map_path='ann_dir/13'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/13' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/14/20240918_220236/20240918_220236.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/14/20240918_220236/20240918_220236.json new file mode 100644 index 0000000000..12a590d269 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/14/20240918_220236/20240918_220236.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.67, "mPr@60.0": 98.67, "mPr@70.0": 95.5, "mPr@80.0": 76.89, "mPr@90.0": 28.11, "mIoU": 84.3, "data_time": 0.01635984012058803, "time": 0.042726448604038784} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/14/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/14/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..a7ce59bcd8 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/14/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/14', seg_map_path='ann_dir/14'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/14' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/15/20240918_220249/20240918_220249.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/15/20240918_220249/20240918_220249.json new file mode 100644 index 0000000000..9f7dfc60fa --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/15/20240918_220249/20240918_220249.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.67, "mPr@60.0": 98.67, "mPr@70.0": 95.22, "mPr@80.0": 80.5, "mPr@90.0": 33.94, "mIoU": 84.73, "data_time": 0.017466204507010325, "time": 0.043459721973964145} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/15/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/15/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..5d5c5791f4 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/15/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/15', seg_map_path='ann_dir/15'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/15' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/16/20240918_220301/20240918_220301.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/16/20240918_220301/20240918_220301.json new file mode 100644 index 0000000000..8db1fbf06f --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/16/20240918_220301/20240918_220301.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.67, "mPr@60.0": 96.61, "mPr@70.0": 89.67, "mPr@80.0": 74.67, "mPr@90.0": 28.94, "mIoU": 83.85, "data_time": 0.01520289693559919, "time": 0.041020563670567105} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/16/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/16/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..71fe028d05 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/16/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/16', seg_map_path='ann_dir/16'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/16' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/17/20240918_220314/20240918_220314.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/17/20240918_220314/20240918_220314.json new file mode 100644 index 0000000000..c5983f8e08 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/17/20240918_220314/20240918_220314.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.33, "mPr@60.0": 97.81, "mPr@70.0": 86.87, "mPr@80.0": 75.42, "mPr@90.0": 30.52, "mIoU": 83.77, "data_time": 0.016160385949271067, "time": 0.04227852821350098} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/17/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/17/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..9b5da3f375 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/17/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/17', seg_map_path='ann_dir/17'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/17' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/18/20240918_220326/20240918_220326.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/18/20240918_220326/20240918_220326.json new file mode 100644 index 0000000000..fb6fd2e7f2 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/18/20240918_220326/20240918_220326.json @@ -0,0 +1 @@ +{"mPr@50.0": 96.77, "mPr@60.0": 95.47, "mPr@70.0": 85.31, "mPr@80.0": 69.69, "mPr@90.0": 23.65, "mIoU": 81.3, "data_time": 0.0166778564453125, "time": 0.04302985327584403} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/18/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/18/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..b9aee5c941 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/18/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/18', seg_map_path='ann_dir/18'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/18' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/19/20240918_220338/20240918_220338.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/19/20240918_220338/20240918_220338.json new file mode 100644 index 0000000000..803d9391a8 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/19/20240918_220338/20240918_220338.json @@ -0,0 +1 @@ +{"mPr@50.0": 96.15, "mPr@60.0": 93.44, "mPr@70.0": 85.36, "mPr@80.0": 67.19, "mPr@90.0": 22.97, "mIoU": 81.23, "data_time": 0.01703940119062151, "time": 0.04374807221548898} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/19/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/19/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..dbf01f9555 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/19/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/19', seg_map_path='ann_dir/19'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/19' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/2/20240918_220351/20240918_220351.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/2/20240918_220351/20240918_220351.json new file mode 100644 index 0000000000..b79d78994e --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/2/20240918_220351/20240918_220351.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 100.0, "mPr@80.0": 100.0, "mPr@90.0": 75.0, "mIoU": 93.56, "data_time": 0.026293107441493442, "time": 0.05340204920087542} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/2/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/2/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..3b7e1e733f --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/2/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/2', seg_map_path='ann_dir/2'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/2' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/20/20240918_220403/20240918_220403.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/20/20240918_220403/20240918_220403.json new file mode 100644 index 0000000000..9017491ec9 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/20/20240918_220403/20240918_220403.json @@ -0,0 +1 @@ +{"mPr@50.0": 95.52, "mPr@60.0": 92.45, "mPr@70.0": 83.07, "mPr@80.0": 63.91, "mPr@90.0": 19.48, "mIoU": 80.26, "data_time": 0.016621180943080356, "time": 0.042001145226614814} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/20/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/20/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..c83724e611 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/20/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/20', seg_map_path='ann_dir/20'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/20' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/3/20240918_220416/20240918_220416.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/3/20240918_220416/20240918_220416.json new file mode 100644 index 0000000000..ce84ce507a --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/3/20240918_220416/20240918_220416.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 100.0, "mPr@80.0": 100.0, "mPr@90.0": 65.38, "mIoU": 92.8, "data_time": 0.018199545996529714, "time": 0.043818575995309014} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/3/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/3/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..40eaed1374 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/3/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/3', seg_map_path='ann_dir/3'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/3' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/4/20240918_220428/20240918_220428.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/4/20240918_220428/20240918_220428.json new file mode 100644 index 0000000000..f20f2f9c67 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/4/20240918_220428/20240918_220428.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 100.0, "mPr@80.0": 96.67, "mPr@90.0": 56.67, "mIoU": 91.92, "data_time": 0.01890397071838379, "time": 0.04447371619088309} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/4/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/4/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..d4dfff8745 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/4/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/4', seg_map_path='ann_dir/4'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/4' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/5/20240918_220441/20240918_220441.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/5/20240918_220441/20240918_220441.json new file mode 100644 index 0000000000..c1a12537bd --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/5/20240918_220441/20240918_220441.json @@ -0,0 +1 @@ +{"mPr@50.0": 100.0, "mPr@60.0": 100.0, "mPr@70.0": 100.0, "mPr@80.0": 97.92, "mPr@90.0": 48.96, "mIoU": 91.19, "data_time": 0.018592493874686106, "time": 0.044320106506347656} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/5/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/5/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..57cfc2e1c2 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/5/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/5', seg_map_path='ann_dir/5'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/5' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/6/20240918_220453/20240918_220453.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/6/20240918_220453/20240918_220453.json new file mode 100644 index 0000000000..4501bad362 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/6/20240918_220453/20240918_220453.json @@ -0,0 +1 @@ +{"mPr@50.0": 96.93, "mPr@60.0": 96.93, "mPr@70.0": 96.93, "mPr@80.0": 95.18, "mPr@90.0": 57.46, "mIoU": 88.59, "data_time": 0.023408753531319753, "time": 0.050176961081368585} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/6/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/6/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..e528006825 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/6/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/6', seg_map_path='ann_dir/6'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/6' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/7/20240918_220506/20240918_220506.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/7/20240918_220506/20240918_220506.json new file mode 100644 index 0000000000..cb804aa52c --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/7/20240918_220506/20240918_220506.json @@ -0,0 +1 @@ +{"mPr@50.0": 97.22, "mPr@60.0": 97.22, "mPr@70.0": 97.22, "mPr@80.0": 95.63, "mPr@90.0": 52.78, "mIoU": 88.45, "data_time": 0.027962582451956614, "time": 0.054694925035749166} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/7/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/7/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..15818cba31 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/7/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/7', seg_map_path='ann_dir/7'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/7' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/8/20240918_220519/20240918_220519.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/8/20240918_220519/20240918_220519.json new file mode 100644 index 0000000000..710660d0d0 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/8/20240918_220519/20240918_220519.json @@ -0,0 +1 @@ +{"mPr@50.0": 97.67, "mPr@60.0": 97.67, "mPr@70.0": 97.67, "mPr@80.0": 90.33, "mPr@90.0": 56.33, "mIoU": 87.43, "data_time": 0.02047865731375558, "time": 0.04789584023611886} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/8/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/8/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..ed9551e4c8 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/8/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/8', seg_map_path='ann_dir/8'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/8' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9/20240918_220531/20240918_220531.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9/20240918_220531/20240918_220531.json new file mode 100644 index 0000000000..c748177e36 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9/20240918_220531/20240918_220531.json @@ -0,0 +1 @@ +{"mPr@50.0": 98.2, "mPr@60.0": 98.2, "mPr@70.0": 98.2, "mPr@80.0": 97.2, "mPr@90.0": 46.53, "mIoU": 84.83, "data_time": 0.02453766550336565, "time": 0.05393382481166294} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..f871598c94 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,341 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/9', seg_map_path='ann_dir/9'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/9' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/clutter_data.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/clutter_data.json new file mode 100644 index 0000000000..e6bb691b2b --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/clutter/clutter_data.json @@ -0,0 +1,202 @@ +{ + "1": { + 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a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py new file mode 100644 index 0000000000..be5f8a1dd6 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512.py @@ -0,0 +1,347 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_results.json b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_results.json new file mode 100644 index 0000000000..27b58bd097 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/segnext_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50.0": 97.22, + "mPr@60.0": 95.79, + "mPr@70.0": 91.39, + "mPr@80.0": 77.1, + "mPr@90.0": 35.74, + "mIoU": 84.36, + "average_fps": 54.12, + "fps_variance": 4.4193, + "average_mem": 191.9, + "mem_variance": 0.09 +} \ No newline at end of file diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/show/vis_data/config.py b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..be5f8a1dd6 --- /dev/null +++ b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/show/vis_data/config.py @@ -0,0 +1,347 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=32, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512' diff --git a/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/show/vis_data/vis_image/test_floor_bottom_seq03_0.png_1.png b/my_projects/test_results/arid20_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_arid20_cat-512x512/show/vis_data/vis_image/test_floor_bottom_seq03_0.png_1.png new file mode 100644 index 0000000000..4156c23c8a Binary files /dev/null and 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+ "IoU": 81.83 + }, + "can_jumbo": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 33.33, + "IoU": 87.15 + }, + "can_pepsi": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 75.0, + "IoU": 90.77 + }, + "cup_black": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 0.0, + "IoU": 86.62 + }, + "cup_glass": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 71.43, + "Pr@90": 14.29, + "IoU": 84.23 + }, + "cup_red": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 83.33, + "Pr@80": 33.33, + "Pr@90": 16.67, + "IoU": 79.76 + }, + "fork_black": { + "Pr@50": 25.0, + "Pr@60": 25.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 42.13 + }, + "fork_silver": { + "Pr@50": 20.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 42.19 + }, + "juice_box_green": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 25.0, + "IoU": 90.2 + }, + "juice_box_orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 40.0, + "IoU": 86.53 + }, + "juice_box_pink": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 80.0, + "Pr@90": 60.0, + "IoU": 88.98 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 94.42 + }, + "knife": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 55.16 + }, + "laptop": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.74 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 50.0, + "Pr@90": 0.0, + "IoU": 83.02 + }, + "marker_blue": { + "Pr@50": 80.0, + "Pr@60": 60.0, + "Pr@70": 60.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 61.37 + }, + "marker_red": { + "Pr@50": 75.0, + "Pr@60": 75.0, + "Pr@70": 75.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 57.18 + }, + "milk_big": { + "Pr@50": 100.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 0.0, + "IoU": 68.03 + }, + "milk_small": { + "Pr@50": 75.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 25.0, + "IoU": 64.75 + }, + "monitor": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 60.0, + "IoU": 94.45 + }, + "mouse_black": { + "Pr@50": 60.0, + "Pr@60": 60.0, + "Pr@70": 20.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 49.83 + }, + "mouse_silver": { + "Pr@50": 60.0, + "Pr@60": 60.0, + "Pr@70": 40.0, + "Pr@80": 20.0, + "Pr@90": 0.0, + "IoU": 46.37 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 0.0, + "IoU": 83.48 + }, + "peach": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 66.67, + "Pr@90": 0.0, + "IoU": 84.37 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 0.0, + "IoU": 88.07 + }, + "pen_black": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 21.31 + }, + "pen_blue": { + "Pr@50": 25.0, + "Pr@60": 25.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 40.7 + }, + "pen_red": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 33.33, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 57.5 + }, + "plate_big": { + "Pr@50": 75.0, + "Pr@60": 75.0, + "Pr@70": 75.0, + "Pr@80": 75.0, + "Pr@90": 50.0, + "IoU": 87.8 + }, + "plate_wide": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 94.85 + }, + "pringles_hot": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 0.0, + "IoU": 76.74 + }, + "pringles_red": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 80.0, + "Pr@90": 80.0, + "IoU": 89.94 + }, + "pringles_purple": { + "Pr@50": 80.0, + "Pr@60": 80.0, + "Pr@70": 80.0, + "Pr@80": 80.0, + "Pr@90": 40.0, + "IoU": 80.33 + }, + "scissors_black": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 33.33, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 58.28 + }, + "scissors_silver": { + "Pr@50": 66.67, + "Pr@60": 66.67, + "Pr@70": 33.33, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 52.06 + }, + "spoon_blue": { + "Pr@50": 66.67, + "Pr@60": 66.67, + "Pr@70": 33.33, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 56.76 + }, + "spoon_silver": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 24.99 + }, + "stapler": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 25.0, + "Pr@90": 0.0, + "IoU": 79.62 + } +} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512.py b/my_projects/test_results/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..7b52f4a329 --- /dev/null +++ b/my_projects/test_results/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512.py @@ -0,0 +1,339 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512' diff --git a/my_projects/test_results/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_results.json b/my_projects/test_results/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_results.json new file mode 100644 index 0000000000..c076778765 --- /dev/null +++ b/my_projects/test_results/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/bisenetv1_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50.0": 81.03, + "mPr@60.0": 78.58, + "mPr@70.0": 73.58, + "mPr@80.0": 56.2, + "mPr@90.0": 27.11, + "mIoU": 74.18, + "average_fps": 96.07, + "fps_variance": 2.0939, + "average_mem": 203.7, + "mem_variance": 0.21 +} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/confusion_matrix/confusion.json 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a/my_projects/test_results/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/show/vis_data/config.py b/my_projects/test_results/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..7b52f4a329 --- /dev/null +++ b/my_projects/test_results/hots_v1/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/show/vis_data/config.py @@ -0,0 +1,339 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) 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100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 80.0, + "Pr@90": 80.0, + "IoU": 90.68 + }, + "pringles_purple": { + "Pr@50": 80.0, + "Pr@60": 80.0, + "Pr@70": 60.0, + "Pr@80": 60.0, + "Pr@90": 60.0, + "IoU": 64.84 + }, + "scissors_black": { + "Pr@50": 66.67, + "Pr@60": 66.67, + "Pr@70": 66.67, + "Pr@80": 66.67, + "Pr@90": 0.0, + "IoU": 77.78 + }, + "scissors_silver": { + "Pr@50": 66.67, + "Pr@60": 66.67, + "Pr@70": 66.67, + "Pr@80": 66.67, + "Pr@90": 0.0, + "IoU": 64.89 + }, + "spoon_blue": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 66.67, + "Pr@90": 0.0, + "IoU": 81.96 + }, + "spoon_silver": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 66.67, + "Pr@90": 0.0, + "IoU": 81.37 + }, + "stapler": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 25.0, + "IoU": 89.91 + } +} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1/mask2former_r50_1bx2-pre-ade20k-10k_hots-v1-512x512/mask2former_r50_1bx2-pre-ade20k-10k_hots-v1-512x512.py b/my_projects/test_results/hots_v1/mask2former_r50_1bx2-pre-ade20k-10k_hots-v1-512x512/mask2former_r50_1bx2-pre-ade20k-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..1d8e08c2fd --- /dev/null +++ b/my_projects/test_results/hots_v1/mask2former_r50_1bx2-pre-ade20k-10k_hots-v1-512x512/mask2former_r50_1bx2-pre-ade20k-10k_hots-v1-512x512.py @@ -0,0 +1,453 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git 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'/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git 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b/my_projects/test_results/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "pen_black", + "pred_label": "pen_blue", + "score": 49.14586070959264 + }, + { + "gt_label": "pen_blue", + "pred_label": "pen_red", + "score": 36.303431667670075 + }, + { + "gt_label": "spoon_blue", + "pred_label": "spoon_silver", + "score": 33.553719008264466 + }, + { + "gt_label": "scissors_silver", + "pred_label": "mouse_black", + "score": 22.671190623072178 + }, + { + "gt_label": "juice_box_orange", + "pred_label": "juice_box_pink", + "score": 18.98148148148148 + }, + { + "gt_label": "pen_blue", + "pred_label": "marker_blue", + "score": 18.543046357615893 + }, + { + "gt_label": "milk_small", + "pred_label": "milk_big", + "score": 14.406298190081973 + }, + { + "gt_label": "fork_silver", + "pred_label": "spoon_silver", + "score": 14.163866632204705 + }, + { + "gt_label": "scissors_black", + "pred_label": 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33.33, + "Pr@80": 33.33, + "Pr@90": 0.0, + "IoU": 71.97 + }, + "scissors_silver": { + "Pr@50": 66.67, + "Pr@60": 66.67, + "Pr@70": 66.67, + "Pr@80": 66.67, + "Pr@90": 0.0, + "IoU": 65.38 + }, + "spoon_blue": { + "Pr@50": 66.67, + "Pr@60": 66.67, + "Pr@70": 66.67, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 50.91 + }, + "spoon_silver": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 33.33, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 49.02 + }, + "stapler": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 75.0, + "Pr@90": 25.0, + "IoU": 86.09 + } +} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_results.json b/my_projects/test_results/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_results.json new file mode 100644 index 0000000000..0f474aab14 --- /dev/null +++ b/my_projects/test_results/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50.0": 88.16, + "mPr@60.0": 86.67, + "mPr@70.0": 84.22, + "mPr@80.0": 74.65, + "mPr@90.0": 57.21, + "mIoU": 80.35, + "average_fps": 22.65, + "fps_variance": 0.0397, + "average_mem": 571.1, + "mem_variance": 0.09 +} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512.py b/my_projects/test_results/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..01e8214fed --- /dev/null +++ b/my_projects/test_results/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512.py @@ -0,0 +1,543 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) 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b/my_projects/test_results/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/config.py @@ -0,0 +1,543 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=47, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 47 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/test_results/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/vis_image/test_kitchen_6_top_raw_0.png_1.png b/my_projects/test_results/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/vis_image/test_kitchen_6_top_raw_0.png_1.png new file mode 100644 index 0000000000..168557f7ab Binary files /dev/null and b/my_projects/test_results/hots_v1/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/vis_image/test_kitchen_6_top_raw_0.png_1.png differ diff --git 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33.33, + "Pr@90": 0.0, + "IoU": 77.61 + }, + "spoon_silver": { + "Pr@50": 33.33, + "Pr@60": 33.33, + "Pr@70": 33.33, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 51.55 + }, + "stapler": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 0.0, + "IoU": 85.33 + } +} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_mit-b0_1xb2-10k_hots-v1-512x512.py b/my_projects/test_results/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_mit-b0_1xb2-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..0cdac1b160 --- /dev/null +++ b/my_projects/test_results/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_mit-b0_1xb2-10k_hots-v1-512x512.py @@ -0,0 +1,325 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection/segformer_mit-b0_1xb2-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/segformer_mit-b0_1xb2-10k_hots-v1-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/segformer_mit-b0_1xb2-10k_hots-v1-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/segformer_mit-b0_1xb2-10k_hots-v1-512x512' diff --git a/my_projects/test_results/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_results.json b/my_projects/test_results/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_results.json new file mode 100644 index 0000000000..7f26bd9e2a --- /dev/null +++ b/my_projects/test_results/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/segformer_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50.0": 90.85, + "mPr@60.0": 86.81, + "mPr@70.0": 83.65, + "mPr@80.0": 67.44, + "mPr@90.0": 30.52, + "mIoU": 78.41, + "average_fps": 64.92, + "fps_variance": 0.3872, + "average_mem": 354.3, + "mem_variance": 0.41 +} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/show/vis_data/config.py b/my_projects/test_results/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..0cdac1b160 --- /dev/null +++ b/my_projects/test_results/hots_v1/segformer_mit-b0_1xb2-10k_hots-v1-512x512/show/vis_data/config.py @@ -0,0 +1,325 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection/segformer_mit-b0_1xb2-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=47, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/segformer_mit-b0_1xb2-10k_hots-v1-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/segformer_mit-b0_1xb2-10k_hots-v1-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 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"Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 67.53 + }, + "spoon_silver": { + "Pr@50": 66.67, + "Pr@60": 66.67, + "Pr@70": 33.33, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 62.06 + }, + "stapler": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 25.0, + "IoU": 87.13 + } +} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512.py b/my_projects/test_results/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512.py new file mode 100644 index 0000000000..6faedae4ac --- /dev/null +++ b/my_projects/test_results/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512.py @@ -0,0 +1,347 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=47, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git a/my_projects/test_results/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_results.json b/my_projects/test_results/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_results.json new file mode 100644 index 0000000000..f3040a8bd6 --- /dev/null +++ b/my_projects/test_results/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/segnext_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50.0": 95.25, + "mPr@60.0": 94.54, + "mPr@70.0": 87.66, + "mPr@80.0": 74.59, + "mPr@90.0": 47.64, + "mIoU": 84.3, + "average_fps": 54.78, + "fps_variance": 0.0278, + "average_mem": 190.1, + "mem_variance": 0.09 +} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/config.py b/my_projects/test_results/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..6faedae4ac --- /dev/null +++ b/my_projects/test_results/hots_v1/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/show/vis_data/config.py @@ -0,0 +1,347 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/' +dataset_type = 'HOTSDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=47, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/HOTS_v1/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-512x512' diff --git 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file mode 100644 index 0000000000..00780ed334 --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/20240925_165258/20240925_165258.json @@ -0,0 +1 @@ +{"mPr@50.0": 92.24, "mPr@60.0": 87.18, "mPr@70.0": 80.0, "mPr@80.0": 61.9, "mPr@90.0": 29.02, "mIoU": 80.17, "data_time": 0.8086125570185044, "time": 0.9428172392003676} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/benchmark/fps_20240925_165326.json b/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/benchmark/fps_20240925_165326.json new file mode 100644 index 0000000000..152af84cb2 --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/benchmark/fps_20240925_165326.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512.py", + "unit": "img / s", + "overall_fps_1": 95.89, + "overall_memory_1": 204, + "overall_fps_2": 96.6, + "overall_memory_2": 206, + "overall_fps_3": 96.57, + "overall_memory_3": 205, + "overall_fps_4": 94.6, + "overall_memory_4": 205, + "overall_fps_5": 95.94, + "overall_memory_5": 206, + "overall_fps_6": 95.37, + "overall_memory_6": 204, + "overall_fps_7": 95.82, + "overall_memory_7": 205, + "overall_fps_8": 95.97, + "overall_memory_8": 205, + "overall_fps_9": 94.65, + "overall_memory_9": 204, + "overall_fps_10": 95.2, + "overall_memory_10": 205, + "average_fps": 95.66, + "fps_variance": 0.4409, + "average_mem": 204.9, + "mem_variance": 0.49 +} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_label_results.json b/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_label_results.json new file mode 100644 index 0000000000..c14bc32de8 --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_label_results.json @@ -0,0 +1,210 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.96 + }, + "apple": { + "Pr@50": 66.67, + "Pr@60": 66.67, + "Pr@70": 66.67, + "Pr@80": 33.33, + "Pr@90": 33.33, + "IoU": 68.09 + }, + "banana": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 66.67, + "Pr@80": 33.33, + "Pr@90": 0.0, + "IoU": 77.74 + }, + "book": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 87.5, + "IoU": 92.34 + }, + "bowl": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 33.33, + "IoU": 88.21 + }, + "can": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 70.0, + "Pr@90": 20.0, + "IoU": 84.41 + }, + "cup": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 77.78, + "Pr@90": 0.0, + "IoU": 83.41 + }, + "fork": { + "Pr@50": 83.33, + "Pr@60": 66.67, + "Pr@70": 16.67, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 59.14 + }, + "juice_box": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 57.14, + "IoU": 90.03 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 93.7 + }, + "knife": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 60.49 + }, + "laptop": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 80.0, + "IoU": 92.41 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 0.0, + "IoU": 84.65 + }, + "marker": { + "Pr@50": 80.0, + "Pr@60": 80.0, + "Pr@70": 80.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 67.89 + }, + "milk": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 75.0, + "Pr@90": 25.0, + "IoU": 84.45 + }, + "monitor": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 80.0, + "Pr@90": 80.0, + "IoU": 92.04 + }, + "mouse": { + "Pr@50": 100.0, + "Pr@60": 80.0, + "Pr@70": 60.0, + "Pr@80": 40.0, + "Pr@90": 0.0, + "IoU": 74.84 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 75.0, + "Pr@90": 0.0, + "IoU": 81.83 + }, + "peach": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 66.67, + "Pr@90": 0.0, + "IoU": 84.73 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 0.0, + "IoU": 83.76 + }, + "pen": { + "Pr@50": 83.33, + "Pr@60": 33.33, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 57.11 + }, + "plate": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 80.0, + "IoU": 93.62 + }, + "pringles": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 83.33, + "Pr@90": 33.33, + "IoU": 89.22 + }, + "scissors": { + "Pr@50": 60.0, + "Pr@60": 40.0, + "Pr@70": 40.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 60.99 + }, + "spoon": { + "Pr@50": 75.0, + "Pr@60": 50.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 59.6 + }, + "stapler": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 75.0, + "Pr@90": 25.0, + "IoU": 81.91 + } +} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512.py b/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512.py new file mode 100644 index 0000000000..b5d17d0dae --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512.py @@ -0,0 +1,341 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512' diff --git a/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_results.json b/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_results.json new file mode 100644 index 0000000000..be3bb19366 --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/bisenetv1_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50.0": 92.24, + "mPr@60.0": 87.18, + "mPr@70.0": 80.0, + "mPr@80.0": 61.9, + "mPr@90.0": 29.02, + "mIoU": 80.17, + "average_fps": 95.66, + "fps_variance": 0.4409, + "average_mem": 204.9, + "mem_variance": 0.49 +} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/clutter/clutter_data.json b/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/clutter/clutter_data.json new file mode 100644 index 0000000000..9e26dfeeb6 --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/clutter/clutter_data.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json b/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..d128be0083 --- /dev/null +++ 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b/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/flops/flops_20240925_165432.json new file mode 100644 index 0000000000..1edb374dce --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/flops/flops_20240925_165432.json @@ -0,0 +1,13 @@ +{ + "ori_shape": [ + 512, + 512 + ], + "pad_shape": [ + 512, + 512 + ], + "flops": "14.846G", + "params": "13.277M", + "compute_type": "direct: randomly generate a picture" +} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/show/vis_data/config.py b/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..b5d17d0dae --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/show/vis_data/config.py @@ -0,0 +1,341 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/hots_v1_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_hots-v1-cat-512x512' diff --git a/my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_165439/20240925_165439.json b/my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_165439/20240925_165439.json new file mode 100644 index 0000000000..fec20262d2 --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_165439/20240925_165439.json @@ -0,0 +1 @@ +{"mPr@50.0": 97.95, "mPr@60.0": 97.95, "mPr@70.0": 95.38, "mPr@80.0": 87.44, "mPr@90.0": 60.66, "mIoU": 88.98, "data_time": 0.8086580248440013, "time": 1.0142073210547953} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/benchmark/fps_20240925_165511.json b/my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/benchmark/fps_20240925_165511.json new file mode 100644 index 0000000000..9aa40b3f12 --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/benchmark/fps_20240925_165511.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py", + "unit": "img / s", + "overall_fps_1": 14.47, + "overall_memory_1": 577, + "overall_fps_2": 14.27, + "overall_memory_2": 577, + "overall_fps_3": 14.01, + "overall_memory_3": 575, + "overall_fps_4": 14.07, + "overall_memory_4": 578, + "overall_fps_5": 14.17, + "overall_memory_5": 578, + "overall_fps_6": 14.1, + "overall_memory_6": 578, + "overall_fps_7": 14.16, + "overall_memory_7": 578, + "overall_fps_8": 14.19, + "overall_memory_8": 578, + "overall_fps_9": 14.12, + "overall_memory_9": 578, + "overall_fps_10": 14.03, + "overall_memory_10": 578, + "average_fps": 14.16, + "fps_variance": 0.0165, + "average_mem": 577.5, + "mem_variance": 0.85 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a/my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/top_10_confusion.json b/my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..1bc26b7852 --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "apple", + "pred_label": "pear", + "score": 4.147465437788019 + }, + { + "gt_label": "scissors", + "pred_label": "pringles", + "score": 2.2839801948570515 + }, + { + "gt_label": "banana", + "pred_label": "stapler", + "score": 2.1410286246218293 + }, + { + "gt_label": "banana", + "pred_label": "pringles", + "score": 2.0013963230160576 + }, + { + "gt_label": "fork", + "pred_label": "pen", + "score": 1.4912019087384432 + }, + { + "gt_label": "scissors", + "pred_label": "stapler", + "score": 1.4214981632327104 + }, + { + "gt_label": "mouse", + "pred_label": "can", + "score": 1.4193392522800026 + }, + { + "gt_label": "scissors", + "pred_label": "pen", + "score": 1.357610605334611 + }, + { + "gt_label": "cup", + "pred_label": "juice_box", + "score": 0.3860175852455501 + }, + { + "gt_label": "mouse", + "pred_label": "keyboard", + "score": 0.2415896599625536 + } +] \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_label_results.json b/my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_label_results.json new file mode 100644 index 0000000000..cc9a5a614f --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_label_results.json @@ -0,0 +1,210 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 98.81 + }, + "apple": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 66.67, + "Pr@80": 66.67, + "Pr@90": 0.0, + "IoU": 85.04 + }, + "banana": { + "Pr@50": 66.67, + "Pr@60": 66.67, + "Pr@70": 66.67, + "Pr@80": 66.67, + "Pr@90": 33.33, + "IoU": 70.42 + }, + "book": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.46 + }, + "bowl": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 83.33, + "IoU": 92.75 + }, + "can": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 92.16 + }, + "cup": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 88.89, + "IoU": 91.93 + }, + "fork": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 83.33, + "Pr@80": 33.33, + "Pr@90": 0.0, + "IoU": 78.61 + }, + "juice_box": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 94.44 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.03 + }, + "knife": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 0.0, + "IoU": 85.85 + }, + "laptop": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.51 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 90.56 + }, + "marker": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 60.0, + "Pr@90": 0.0, + "IoU": 79.53 + }, + "milk": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 93.9 + }, + "monitor": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.16 + }, + "mouse": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 80.0, + "IoU": 90.4 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 50.0, + "IoU": 89.18 + }, + "peach": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 66.67, + "IoU": 89.92 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 50.0, + "IoU": 89.45 + }, + "pen": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 83.33, + "Pr@80": 16.67, + "Pr@90": 0.0, + "IoU": 75.73 + }, + "plate": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.24 + }, + "pringles": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 93.79 + }, + "scissors": { + "Pr@50": 80.0, + "Pr@60": 80.0, + "Pr@70": 80.0, + "Pr@80": 80.0, + "Pr@90": 0.0, + "IoU": 78.84 + }, + "spoon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 75.0, + "Pr@90": 0.0, + "IoU": 82.33 + }, + "stapler": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 75.0, + "Pr@90": 25.0, + "IoU": 88.41 + } +} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py b/my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py new file mode 100644 index 0000000000..279d414121 --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py @@ -0,0 +1,434 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_results.json b/my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_results.json new file mode 100644 index 0000000000..80445935ed --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/mask2former_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50.0": 97.95, + "mPr@60.0": 97.95, + "mPr@70.0": 95.38, + "mPr@80.0": 87.44, + "mPr@90.0": 60.66, + "mIoU": 88.98, + "average_fps": 14.16, + "fps_variance": 0.0165, + "average_mem": 577.5, + "mem_variance": 0.85 +} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show/vis_data/config.py b/my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..279d414121 --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show/vis_data/config.py @@ -0,0 +1,434 @@ +auto_scale_lr = dict(base_batch_size=2, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/hots_v1_cat/mask2former_r50_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_165823/20240925_165823.json b/my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_165823/20240925_165823.json new file mode 100644 index 0000000000..030b92d890 --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_165823/20240925_165823.json @@ -0,0 +1 @@ +{"mPr@50.0": 96.99, "mPr@60.0": 95.38, "mPr@70.0": 93.78, "mPr@80.0": 80.67, "mPr@90.0": 56.91, "mIoU": 87.74, "data_time": 0.7623629009022432, "time": 0.9669730102314669} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/benchmark/fps_20240925_165855.json b/my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/benchmark/fps_20240925_165855.json new file mode 100644 index 0000000000..525a5e501f --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/benchmark/fps_20240925_165855.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py", + "unit": "img / s", + "overall_fps_1": 21.48, + "overall_memory_1": 543, + "overall_fps_2": 21.5, + "overall_memory_2": 543, + "overall_fps_3": 21.3, + "overall_memory_3": 544, + "overall_fps_4": 21.24, + "overall_memory_4": 543, + "overall_fps_5": 21.22, + "overall_memory_5": 546, + "overall_fps_6": 21.17, + "overall_memory_6": 543, + "overall_fps_7": 21.05, + "overall_memory_7": 545, + "overall_fps_8": 20.89, + "overall_memory_8": 543, + "overall_fps_9": 20.79, + "overall_memory_9": 546, + "overall_fps_10": 20.97, + "overall_memory_10": 546, + "average_fps": 21.16, + "fps_variance": 0.0507, + "average_mem": 544.2, + "mem_variance": 1.76 +} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/clutter/clutter_data.json b/my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/clutter/clutter_data.json new file mode 100644 index 0000000000..9e26dfeeb6 --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/clutter/clutter_data.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json b/my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..3e06a12d41 --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json @@ 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a/my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py b/my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py new file mode 100644 index 0000000000..216a57e890 --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py @@ -0,0 +1,524 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show/vis_data/config.py b/my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..216a57e890 --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show/vis_data/config.py @@ -0,0 +1,524 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=26, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 26 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/hots_v1_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240925_170119/20240925_170119.json b/my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240925_170119/20240925_170119.json new file mode 100644 index 0000000000..4c1c55191c --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/20240925_170119/20240925_170119.json @@ -0,0 +1 @@ +{"mPr@50.0": 94.42, "mPr@60.0": 93.14, "mPr@70.0": 85.51, "mPr@80.0": 75.3, "mPr@90.0": 35.25, "mIoU": 84.22, "data_time": 0.7725295880261589, "time": 0.9281919142779183} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/benchmark/fps_20240925_170146.json b/my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/benchmark/fps_20240925_170146.json new file mode 100644 index 0000000000..49c13d6c6d --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/benchmark/fps_20240925_170146.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512.py", + "unit": "img / s", + "overall_fps_1": 62.81, + "overall_memory_1": 355, + "overall_fps_2": 62.7, + "overall_memory_2": 356, + "overall_fps_3": 62.91, + "overall_memory_3": 354, + "overall_fps_4": 62.64, + "overall_memory_4": 354, + "overall_fps_5": 62.69, + "overall_memory_5": 354, + "overall_fps_6": 62.73, + "overall_memory_6": 354, + "overall_fps_7": 62.76, + "overall_memory_7": 354, + "overall_fps_8": 62.56, + "overall_memory_8": 354, + "overall_fps_9": 62.37, + "overall_memory_9": 354, + "overall_fps_10": 61.77, + "overall_memory_10": 354, + "average_fps": 62.59, + "fps_variance": 0.095, + "average_mem": 354.3, + "mem_variance": 0.41 +} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/clutter/clutter_data.json b/my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/clutter/clutter_data.json new file mode 100644 index 0000000000..9e26dfeeb6 --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/clutter/clutter_data.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json b/my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..dc83499bdd --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json @@ -0,0 +1,3252 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.002284458965522386 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.006200674334989333 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 0.09151822345551924 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.017483104327977443 + }, + { + "gt_label": "_background_", + "pred_label": "can", + "score": 0.07191383580241388 + }, + { + "gt_label": "_background_", + "pred_label": "cup", + "score": 0.05412769099941816 + }, + { + "gt_label": "_background_", + "pred_label": "fork", + "score": 0.012751010756538214 + }, + { + "gt_label": "_background_", + "pred_label": "juice_box", + "score": 0.07802126691432067 + }, + { + "gt_label": "_background_", + 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a/my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512.py b/my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512.py new file mode 100644 index 0000000000..84fc4e353b --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512.py @@ -0,0 +1,327 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512' diff --git a/my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/segformer_results.json b/my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/segformer_results.json new file mode 100644 index 0000000000..1ac9e93b31 --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/segformer_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50.0": 94.42, + "mPr@60.0": 93.14, + "mPr@70.0": 85.51, + "mPr@80.0": 75.3, + "mPr@90.0": 35.25, + "mIoU": 84.22, + "average_fps": 62.59, + "fps_variance": 0.095, + "average_mem": 354.3, + "mem_variance": 0.41 +} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/show/vis_data/config.py b/my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..84fc4e353b --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/show/vis_data/config.py @@ -0,0 +1,327 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=26, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/hots_v1_cat/segformer_mit-b0_1xb2-10k_hots-v1-cat-512x512' diff --git a/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_170304/20240925_170304.json b/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_170304/20240925_170304.json new file mode 100644 index 0000000000..0ff0068a3e --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/20240925_170304/20240925_170304.json @@ -0,0 +1 @@ +{"mPr@50.0": 97.31, "mPr@60.0": 93.97, "mPr@70.0": 91.6, "mPr@80.0": 81.15, "mPr@90.0": 55.56, "mIoU": 87.7, "data_time": 0.7878677003523883, "time": 0.9210841375238755} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/benchmark/fps_20240925_170332.json b/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/benchmark/fps_20240925_170332.json new file mode 100644 index 0000000000..25c68c6c7a --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/benchmark/fps_20240925_170332.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py", + "unit": "img / s", + "overall_fps_1": 51.42, + "overall_memory_1": 191, + "overall_fps_2": 51.64, + "overall_memory_2": 192, + "overall_fps_3": 51.92, + "overall_memory_3": 192, + "overall_fps_4": 51.57, + "overall_memory_4": 192, + "overall_fps_5": 51.49, + "overall_memory_5": 192, + "overall_fps_6": 51.16, + "overall_memory_6": 192, + "overall_fps_7": 51.31, + "overall_memory_7": 192, + "overall_fps_8": 51.62, + "overall_memory_8": 192, + "overall_fps_9": 51.57, + "overall_memory_9": 192, + "overall_fps_10": 51.16, + "overall_memory_10": 192, + "average_fps": 51.49, + "fps_variance": 0.049, + "average_mem": 191.9, + "mem_variance": 0.09 +} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/clutter/clutter_data.json b/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/clutter/clutter_data.json new file mode 100644 index 0000000000..9e26dfeeb6 --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/clutter/clutter_data.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json b/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 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b/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/flops/flops_20240925_170452.json @@ -0,0 +1,13 @@ +{ + "ori_shape": [ + 512, + 512 + ], + "pad_shape": [ + 512, + 512 + ], + "flops": "6.32G", + "params": "4.232M", + "compute_type": "direct: randomly generate a picture" +} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_label_results.json b/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_label_results.json new file mode 100644 index 0000000000..2b96f33831 --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_label_results.json @@ -0,0 +1,210 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 98.76 + }, + "apple": { + "Pr@50": 100.0, + "Pr@60": 100.0, + 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"Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.1 + }, + "knife": { + "Pr@50": 100.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 50.0, + "IoU": 77.83 + }, + "laptop": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.3 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 50.0, + "IoU": 90.94 + }, + "marker": { + "Pr@50": 100.0, + "Pr@60": 80.0, + "Pr@70": 80.0, + "Pr@80": 60.0, + "Pr@90": 0.0, + "IoU": 75.37 + }, + "milk": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 92.96 + }, + "monitor": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.58 + }, + "mouse": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 80.0, + "Pr@80": 80.0, + "Pr@90": 40.0, + "IoU": 85.62 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 25.0, + "IoU": 88.87 + }, + "peach": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 33.33, + "IoU": 88.94 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 50.0, + "IoU": 90.15 + }, + "pen": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 83.33, + "Pr@80": 16.67, + "Pr@90": 0.0, + "IoU": 75.75 + }, + "plate": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.07 + }, + "pringles": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 83.33, + "IoU": 93.39 + }, + "scissors": { + "Pr@50": 80.0, + "Pr@60": 80.0, + "Pr@70": 80.0, + "Pr@80": 20.0, + "Pr@90": 0.0, + "IoU": 73.7 + }, + "spoon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 75.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 74.93 + }, + "stapler": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 25.0, + "IoU": 88.26 + } +} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py b/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py new file mode 100644 index 0000000000..089979fe4e --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512.py @@ -0,0 +1,349 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=26, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_results.json b/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_results.json new file mode 100644 index 0000000000..a0536cfdd1 --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/segnext_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50.0": 97.31, + "mPr@60.0": 93.97, + "mPr@70.0": 91.6, + "mPr@80.0": 81.15, + "mPr@90.0": 55.56, + "mIoU": 87.7, + "average_fps": 51.49, + "fps_variance": 0.049, + "average_mem": 191.9, + "mem_variance": 0.09 +} \ No newline at end of file diff --git a/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show/vis_data/config.py b/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..089979fe4e --- /dev/null +++ b/my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show/vis_data/config.py @@ -0,0 +1,349 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=26, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=2500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/HOTS_v1_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='HOTSCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/hots_v1_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_hots-v1-cat-512x512' diff --git a/my_projects/test_results/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/20240810_122612/20240810_122612.json b/my_projects/test_results/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/20240810_122612/20240810_122612.json new file mode 100644 index 0000000000..1946d60988 --- /dev/null +++ b/my_projects/test_results/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/20240810_122612/20240810_122612.json @@ -0,0 +1 @@ +{"mPr@50.0": 85.28, "mPr@60.0": 85.0, "mPr@70.0": 84.11, "mPr@80.0": 79.43, "mPr@90.0": 46.68, "mIoU": 74.85, "data_time": 0.761435539949508, "time": 0.802395795072828} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/benchmark/fps_20240905_123957.json b/my_projects/test_results/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/benchmark/fps_20240905_123957.json new file mode 100644 index 0000000000..15e41b4ba0 --- /dev/null +++ b/my_projects/test_results/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/benchmark/fps_20240905_123957.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512.py", + "unit": "img / s", + "overall_fps_1": 91.35, + "overall_memory_1": 242, + "overall_fps_2": 89.1, + "overall_memory_2": 242, + "overall_fps_3": 89.65, + "overall_memory_3": 243, + "overall_fps_4": 93.06, + "overall_memory_4": 241, + "overall_fps_5": 90.12, + "overall_memory_5": 242, + "overall_fps_6": 91.75, + "overall_memory_6": 243, + "overall_fps_7": 91.7, + "overall_memory_7": 241, + "overall_fps_8": 92.57, + "overall_memory_8": 242, + "overall_fps_9": 91.45, + "overall_memory_9": 243, + "overall_fps_10": 86.14, + "overall_memory_10": 241, + "average_fps": 90.69, + "fps_variance": 3.6917, + "average_mem": 242.0, + "mem_variance": 0.6 +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_label_results.json b/my_projects/test_results/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_label_results.json new file mode 100644 index 0000000000..d5691de38c --- /dev/null +++ b/my_projects/test_results/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_label_results.json @@ -0,0 +1,578 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 99.41 + }, + "apple": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 20.0, + "IoU": 90.62 + }, + "banana": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 80.0, + "IoU": 91.44 + }, + "book_1": { + "Pr@50": 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+ "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "pen_violet": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "plate": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.73 + }, + "plate_1": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.87 + }, + "plate_2": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 87.92 + }, + "pringles_green": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 75.0, + "Pr@90": 0.0, + "IoU": 78.51 + }, + "pringles_hot": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 33.33, + "IoU": 89.3 + }, + "pringles_orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 66.67, + "IoU": 89.41 + }, + "scissors": { + "Pr@50": 80.0, + "Pr@60": 80.0, + "Pr@70": 80.0, + "Pr@80": 20.0, + "Pr@90": 0.0, + "IoU": 65.65 + }, + "sponge_green": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 80.51 + }, + "sponge_pink": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 16.67, + "IoU": 86.19 + }, + "sponge_yellow": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 50.0, + "IoU": 73.8 + }, + "spoon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 12.3 + } +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512.py b/my_projects/test_results/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..81a6f4f7f5 --- /dev/null +++ b/my_projects/test_results/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512.py @@ -0,0 +1,342 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512' diff --git a/my_projects/test_results/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_results.json b/my_projects/test_results/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_results.json new file mode 100644 index 0000000000..b3bf122506 --- /dev/null +++ b/my_projects/test_results/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/bisenetv1_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50.0": 85.28, + "mPr@60.0": 85.0, + "mPr@70.0": 84.11, + "mPr@80.0": 79.43, + "mPr@90.0": 46.68, + "mIoU": 74.85, + "average_fps": 90.69, + "fps_variance": 3.6917, + "average_mem": 242.0, + "mem_variance": 0.6 +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json b/my_projects/test_results/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..627b710f91 --- /dev/null +++ b/my_projects/test_results/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/confusion_matrix/confusion.json @@ -0,0 +1,25562 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.0032002475519656735 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.004974028171853624 + }, + { + "gt_label": "_background_", + "pred_label": "book_1", + "score": 0.0030348600815798504 + }, + { + "gt_label": "_background_", + "pred_label": "book_10", + "score": 0.004246323302156003 + }, + { + "gt_label": "_background_", + "pred_label": "book_11", + "score": 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b/my_projects/test_results/irl_vision/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/show/vis_data/config.py @@ -0,0 +1,342 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim-512x512' diff --git 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{ + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.07 + }, + "plate_2": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 84.84 + }, + "pringles_green": { + "Pr@50": 75.0, + "Pr@60": 75.0, + "Pr@70": 75.0, + "Pr@80": 75.0, + "Pr@90": 75.0, + "IoU": 49.47 + }, + "pringles_hot": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.97 + }, + "pringles_orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.17 + }, + "scissors": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.33 + }, + "sponge_green": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.01 + }, + "sponge_pink": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.48 + }, + "sponge_yellow": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 93.89 + }, + "spoon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 50.0, + "IoU": 55.25 + } +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision/mask2former_r50_1bx2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_r50_1bx2-pre-ade20k-10k_irl_vision_sim-512x512.py b/my_projects/test_results/irl_vision/mask2former_r50_1bx2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_r50_1bx2-pre-ade20k-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..6e399ed0ab --- /dev/null +++ b/my_projects/test_results/irl_vision/mask2former_r50_1bx2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_r50_1bx2-pre-ade20k-10k_irl_vision_sim-512x512.py @@ -0,0 +1,481 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/test_results/irl_vision/mask2former_r50_1bx2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_results.json b/my_projects/test_results/irl_vision/mask2former_r50_1bx2-pre-ade20k-10k_irl_vision_sim-512x512/mask2former_results.json new file mode 100644 index 0000000000..4e86ebfeb0 --- /dev/null +++ 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mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/test_results/irl_vision/mask2former_r50_1bx2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/vis_image/test_scene_100.png_1.png b/my_projects/test_results/irl_vision/mask2former_r50_1bx2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/vis_image/test_scene_100.png_1.png new file mode 100644 index 0000000000..66c1d21352 Binary files /dev/null and b/my_projects/test_results/irl_vision/mask2former_r50_1bx2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/vis_image/test_scene_100.png_1.png differ diff --git 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b/my_projects/test_results/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..50623ffd2c --- /dev/null +++ b/my_projects/test_results/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "bowl_red", + "pred_label": "bowl_1", + "score": 57.93196257002472 + }, + { + "gt_label": "pen_violet", + "pred_label": "pen_blue", + "score": 56.77018633540373 + }, + { + "gt_label": "pen_violet", + "pred_label": "scissors", + "score": 37.204968944099384 + }, + { + "gt_label": "juice_box_green", + "pred_label": "pringles_orange", + "score": 28.418386655410323 + }, + { + "gt_label": "apple", + "pred_label": "peach", + "score": 26.50575607585785 + }, + { + "gt_label": "juice_box_orange", + "pred_label": "juice_box_green", + "score": 25.192683768549408 + }, + { + "gt_label": "cup_glass", + "pred_label": "can_coke", + "score": 24.964824120603016 + }, + { + "gt_label": "mug_red", + "pred_label": "bowl_red", + "score": 24.049692078997666 + }, + { + "gt_label": "juice_box_pink", + "pred_label": "can_coke", + "score": 20.810048705460137 + }, + { + "gt_label": "cup_glass_hex", + "pred_label": "plate_1", + "score": 16.509926854754443 + } +] \ No newline at end of file diff --git a/my_projects/test_results/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_label_results.json b/my_projects/test_results/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_label_results.json new file mode 100644 index 0000000000..4c9cf5e5ec --- /dev/null +++ b/my_projects/test_results/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_label_results.json @@ -0,0 +1,578 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, 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a/my_projects/test_results/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_results.json b/my_projects/test_results/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_results.json new file mode 100644 index 0000000000..e74c78575b --- /dev/null +++ b/my_projects/test_results/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50.0": 89.04, + "mPr@60.0": 88.07, + "mPr@70.0": 86.46, + "mPr@80.0": 85.16, + "mPr@90.0": 78.95, + "mIoU": 84.05, + "average_fps": 22.03, + "fps_variance": 0.026, + "average_mem": 606.8, + "mem_variance": 0.36 +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py b/my_projects/test_results/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..57e0dcd9ab --- /dev/null +++ b/my_projects/test_results/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py @@ -0,0 +1,571 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git 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a/my_projects/test_results/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py b/my_projects/test_results/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..57e0dcd9ab --- /dev/null +++ b/my_projects/test_results/irl_vision/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py @@ -0,0 +1,571 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=72, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 72 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git 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"Pr@90": 100.0, + "IoU": 96.07 + }, + "mug_red": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 93.05 + }, + "mug_yellow": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 92.95 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 75.0, + "IoU": 95.85 + }, + "peach": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 66.67, + "IoU": 94.65 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.44 + }, + "pen_black": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 0.0, + "IoU": 83.49 + }, + "pen_blue": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 50.0, + "IoU": 66.73 + }, + "pen_green": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 25.0, + "IoU": 87.91 + }, + "pen_violet": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "plate": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.93 + }, + "plate_1": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.09 + }, + "plate_2": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 86.42 + }, + "pringles_green": { + "Pr@50": 75.0, + "Pr@60": 75.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 50.0, + "IoU": 44.95 + }, + "pringles_hot": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.24 + }, + "pringles_orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 94.37 + }, + "scissors": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 91.26 + }, + "sponge_green": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.1 + }, + "sponge_pink": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 66.67, + "IoU": 92.19 + }, + "sponge_yellow": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 94.61 + }, + "spoon": { + "Pr@50": 75.0, + "Pr@60": 75.0, + "Pr@70": 75.0, + "Pr@80": 50.0, + "Pr@90": 0.0, + "IoU": 47.0 + } +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512.py b/my_projects/test_results/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..8638dfb453 --- /dev/null +++ b/my_projects/test_results/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512.py @@ -0,0 +1,328 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512' diff --git a/my_projects/test_results/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_results.json b/my_projects/test_results/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_results.json new file mode 100644 index 0000000000..ca575076ed --- /dev/null +++ b/my_projects/test_results/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/segformer_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50.0": 93.6, + "mPr@60.0": 92.96, + "mPr@70.0": 92.44, + "mPr@80.0": 90.38, + "mPr@90.0": 78.91, + "mIoU": 87.11, + "average_fps": 62.77, + "fps_variance": 0.0403, + "average_mem": 354.1, + "mem_variance": 0.09 +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/show/vis_data/config.py b/my_projects/test_results/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..8638dfb453 --- /dev/null +++ b/my_projects/test_results/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/show/vis_data/config.py @@ -0,0 +1,328 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=72, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512' diff --git a/my_projects/test_results/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/show/vis_data/vis_image/test_scene_100.png_1.png b/my_projects/test_results/irl_vision/segformer_mit-b0_1xb2-10k_irl_vision_sim-512x512/show/vis_data/vis_image/test_scene_100.png_1.png new file mode 100644 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+ "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.73 + }, + "scissors": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 80.0, + "IoU": 92.97 + }, + "sponge_green": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.08 + }, + "sponge_pink": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 94.42 + }, + "sponge_yellow": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 93.94 + }, + "spoon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 0.0, + "IoU": 54.66 + } +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py b/my_projects/test_results/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py new file mode 100644 index 0000000000..a22638f40b --- /dev/null +++ b/my_projects/test_results/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512.py @@ -0,0 +1,350 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=72, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/test_results/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_results.json b/my_projects/test_results/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_results.json new file mode 100644 index 0000000000..cf406518a6 --- /dev/null +++ b/my_projects/test_results/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/segnext_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50.0": 94.13, + "mPr@60.0": 93.9, + "mPr@70.0": 93.28, + "mPr@80.0": 91.22, + "mPr@90.0": 80.83, + "mIoU": 87.57, + "average_fps": 53.32, + "fps_variance": 0.0221, + "average_mem": 214.0, + "mem_variance": 0.0 +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py b/my_projects/test_results/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..a22638f40b --- /dev/null +++ b/my_projects/test_results/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/config.py @@ -0,0 +1,350 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/' +dataset_type = 'IRLVisionSimDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=72, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512' diff --git a/my_projects/test_results/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/vis_image/test_scene_100.png_1.png b/my_projects/test_results/irl_vision/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim-512x512/show/vis_data/vis_image/test_scene_100.png_1.png new file mode 100644 index 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b/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240925_172729/20240925_172729.json new file mode 100644 index 0000000000..491f899e18 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/20240925_172729/20240925_172729.json @@ -0,0 +1 @@ +{"mPr@50.0": 81.93, "mPr@60.0": 79.83, "mPr@70.0": 78.71, "mPr@80.0": 73.9, "mPr@90.0": 46.61, "mIoU": 71.25, "data_time": 0.767993186201368, "time": 0.8109092627252851} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/benchmark/fps_20240925_172849.json b/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/benchmark/fps_20240925_172849.json new file mode 100644 index 0000000000..4a2a382a90 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/benchmark/fps_20240925_172849.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512.py", + "unit": "img / s", + "overall_fps_1": 92.39, + "overall_memory_1": 204, + "overall_fps_2": 93.01, + "overall_memory_2": 206, + "overall_fps_3": 92.52, + "overall_memory_3": 205, + "overall_fps_4": 92.53, + "overall_memory_4": 205, + "overall_fps_5": 92.19, + "overall_memory_5": 206, + "overall_fps_6": 92.38, + "overall_memory_6": 204, + "overall_fps_7": 91.09, + "overall_memory_7": 205, + "overall_fps_8": 92.6, + "overall_memory_8": 205, + "overall_fps_9": 91.8, + "overall_memory_9": 204, + "overall_fps_10": 91.99, + "overall_memory_10": 205, + "average_fps": 92.25, + "fps_variance": 0.2499, + "average_mem": 204.9, + "mem_variance": 0.49 +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_label_results.json b/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_label_results.json new file mode 100644 index 0000000000..e22422a611 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_label_results.json @@ -0,0 +1,242 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 99.42 + }, + "apple": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 20.0, + "IoU": 90.76 + }, + "banana": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 60.0, + "IoU": 69.73 + }, + "book": { + "Pr@50": 89.66, + "Pr@60": 82.76, + "Pr@70": 82.76, + "Pr@80": 82.76, + "Pr@90": 72.41, + "IoU": 83.73 + }, + "bowl": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 78.12, + "IoU": 93.81 + }, + "can": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 95.24, + "Pr@90": 47.62, + "IoU": 91.18 + }, + "cap": { + "Pr@50": 92.86, + "Pr@60": 92.86, + "Pr@70": 92.86, + "Pr@80": 92.86, + "Pr@90": 78.57, + "IoU": 89.25 + }, + "cereal": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 46.15, + "IoU": 87.83 + }, + "cup": { + "Pr@50": 95.45, + "Pr@60": 95.45, + "Pr@70": 95.45, + "Pr@80": 95.45, + "Pr@90": 31.82, + "IoU": 85.6 + }, + "flashlight": { + "Pr@50": 87.5, + "Pr@60": 81.25, + "Pr@70": 75.0, + "Pr@80": 62.5, + "Pr@90": 18.75, + "IoU": 81.01 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "juice_box": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 77.78, + "IoU": 93.31 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 90.28 + }, + "knife": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "laptop": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 94.44 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 80.0, + "Pr@90": 60.0, + "IoU": 90.72 + }, + "marker": { + "Pr@50": 78.57, + "Pr@60": 78.57, + "Pr@70": 71.43, + "Pr@80": 64.29, + "Pr@90": 28.57, + "IoU": 48.07 + }, + "milk": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 50.0, + "Pr@90": 0.0, + "IoU": 82.65 + }, + "monitor": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.1 + }, + "mouse": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "mug": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 23.53, + "IoU": 87.11 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 75.0, + "IoU": 93.0 + }, + "peach": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 66.67, + "IoU": 92.6 + }, + "pear": { + "Pr@50": 75.0, + "Pr@60": 75.0, + "Pr@70": 75.0, + "Pr@80": 75.0, + "Pr@90": 50.0, + "IoU": 62.71 + }, + "pen": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "plate": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 91.19 + }, + "pringles": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 90.0, + "Pr@90": 30.0, + "IoU": 85.98 + }, + "scissors": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 80.0, + "Pr@80": 40.0, + "Pr@90": 0.0, + "IoU": 64.03 + }, + "sponge": { + "Pr@50": 88.89, + "Pr@60": 88.89, + "Pr@70": 88.89, + "Pr@80": 88.89, + "Pr@90": 33.33, + "IoU": 82.8 + }, + "spoon": { + "Pr@50": 50.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 10.34 + } +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512.py b/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..ae2548c810 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,342 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_results.json b/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_results.json new file mode 100644 index 0000000000..b07ff11154 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/bisenetv1_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50.0": 81.93, + "mPr@60.0": 79.83, + "mPr@70.0": 78.71, + "mPr@80.0": 73.9, + "mPr@90.0": 46.61, + "mIoU": 71.25, + "average_fps": 92.25, + "fps_variance": 0.2499, + "average_mem": 204.9, + "mem_variance": 0.49 +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/clutter/clutter_data.json b/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/clutter/clutter_data.json new file mode 100644 index 0000000000..9e26dfeeb6 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/clutter/clutter_data.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json b/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..a1456279fe --- /dev/null +++ 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b/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "fork", + "pred_label": "spoon", + "score": 63.663147557083235 + }, + { + "gt_label": "knife", + "pred_label": "marker", + "score": 47.61167053963336 + }, + { + "gt_label": "pear", + "pred_label": "banana", + "score": 28.900097336518893 + }, + { + "gt_label": "mouse", + "pred_label": "spoon", + "score": 27.199606040709128 + }, + { + "gt_label": "pen", + "pred_label": "marker", + "score": 25.4673721340388 + }, + { + "gt_label": "knife", + "pred_label": "spoon", + "score": 23.78001549186677 + }, + { + "gt_label": "pen", + "pred_label": "spoon", + "score": 18.365667254556143 + }, + { + "gt_label": "spoon", + "pred_label": "scissors", + "score": 16.93181818181818 + }, + { + "gt_label": "spoon", + "pred_label": "marker", + "score": 16.742424242424242 + }, + { + "gt_label": "pen", + "pred_label": "scissors", + "score": 16.402116402116402 + } +] \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/flops/flops_20240925_173007.json b/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/flops/flops_20240925_173007.json new file mode 100644 index 0000000000..514807dc3e --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/flops/flops_20240925_173007.json @@ -0,0 +1,13 @@ +{ + "ori_shape": [ + 512, + 512 + ], + "pad_shape": [ + 512, + 512 + ], + "flops": "14.85G", + "params": "13.278M", + "compute_type": "direct: randomly generate a picture" +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py b/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..ae2548c810 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py @@ -0,0 +1,342 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/irl_vision_cat/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240925_173015/20240925_173015.json b/my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240925_173015/20240925_173015.json new file mode 100644 index 0000000000..67cd003175 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240925_173015/20240925_173015.json @@ -0,0 +1 @@ +{"mPr@50.0": 93.3, "mPr@60.0": 93.3, "mPr@70.0": 93.3, "mPr@80.0": 92.14, "mPr@90.0": 84.18, "mIoU": 87.49, "data_time": 0.7586687576203119, "time": 0.8658785876773653} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/benchmark/fps_20240925_173141.json b/my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/benchmark/fps_20240925_173141.json new file mode 100644 index 0000000000..222db29bef --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/benchmark/fps_20240925_173141.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py", + "unit": "img / s", + "overall_fps_1": 14.35, + "overall_memory_1": 582, + "overall_fps_2": 14.24, + "overall_memory_2": 581, + "overall_fps_3": 14.21, + "overall_memory_3": 580, + "overall_fps_4": 14.1, + "overall_memory_4": 580, + "overall_fps_5": 14.11, + "overall_memory_5": 581, + "overall_fps_6": 14.1, + "overall_memory_6": 581, + "overall_fps_7": 14.08, + "overall_memory_7": 581, + "overall_fps_8": 14.11, + "overall_memory_8": 581, + "overall_fps_9": 14.05, + "overall_memory_9": 581, + "overall_fps_10": 14.08, + "overall_memory_10": 581, + "average_fps": 14.14, + "fps_variance": 0.0076, + "average_mem": 580.9, + "mem_variance": 0.29 +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/clutter/clutter_data.json b/my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/clutter/clutter_data.json new file mode 100644 index 0000000000..9e26dfeeb6 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/clutter/clutter_data.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json b/my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..b4ca8978bf --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4352 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.0008930923400834438 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.0020177271387070398 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 0.016414706435792924 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.011635008541642643 + }, + { + "gt_label": "_background_", + "pred_label": "can", + "score": 0.0057802920899845105 + }, + { + "gt_label": "_background_", + "pred_label": "cap", + "score": 0.005242782811230587 + }, + { + "gt_label": "_background_", + "pred_label": "cereal", + "score": 0.021140653402067815 + }, + { + "gt_label": "_background_", + "pred_label": "cup", + "score": 0.0053296112331831435 + }, + { + "gt_label": "_background_", + "pred_label": "flashlight", + "score": 0.004663926664880206 + }, + { + "gt_label": "_background_", + "pred_label": "fork", + "score": 0.00031837088049270913 + }, + { + "gt_label": 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b/my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..095e8313eb --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,439 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_results.json b/my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_results.json new file mode 100644 index 0000000000..e60a669976 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/mask2former_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50.0": 93.3, + "mPr@60.0": 93.3, + "mPr@70.0": 93.3, + "mPr@80.0": 92.14, + "mPr@90.0": 84.18, + "mIoU": 87.49, + "average_fps": 14.14, + "fps_variance": 0.0076, + "average_mem": 580.9, + "mem_variance": 0.29 +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py b/my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..095e8313eb --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py @@ -0,0 +1,439 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/irl_vision_cat/mask2former_r50_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/test_results/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240925_173506/20240925_173506.json 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b/my_projects/test_results/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/benchmark/fps_20240925_173630.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py", + "unit": "img / s", + "overall_fps_1": 21.58, + "overall_memory_1": 548, + "overall_fps_2": 21.35, + "overall_memory_2": 548, + "overall_fps_3": 21.21, + "overall_memory_3": 549, + "overall_fps_4": 21.14, + "overall_memory_4": 548, + "overall_fps_5": 21.21, + "overall_memory_5": 550, + "overall_fps_6": 21.01, + "overall_memory_6": 549, + "overall_fps_7": 21.05, + "overall_memory_7": 550, + "overall_fps_8": 21.05, + "overall_memory_8": 551, + "overall_fps_9": 21.05, + "overall_memory_9": 551, + "overall_fps_10": 21.09, + "overall_memory_10": 551, + "average_fps": 21.17, + "fps_variance": 0.0277, + 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"spoon", + "pred_label": "scissors", + "score": 0.0 + }, + { + "gt_label": "spoon", + "pred_label": "sponge", + "score": 0.0 + } +] \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/top_10_confusion.json b/my_projects/test_results/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..a4d180639b --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "peach", + "pred_label": "apple", + "score": 29.038817005545287 + }, + { + "gt_label": "fork", + "pred_label": "spoon", + "score": 16.106064326049598 + }, + { + "gt_label": "fork", + "pred_label": "banana", + "score": 13.331696538178248 + }, + { + "gt_label": 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b/my_projects/test_results/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_label_results.json @@ -0,0 +1,242 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 99.76 + }, + "apple": { + "Pr@50": 80.0, + "Pr@60": 80.0, + "Pr@70": 80.0, + "Pr@80": 80.0, + "Pr@90": 80.0, + "IoU": 68.0 + }, + "banana": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 80.0, + "Pr@90": 80.0, + "IoU": 94.32 + }, + "book": { + "Pr@50": 96.55, + "Pr@60": 96.55, + "Pr@70": 89.66, + "Pr@80": 89.66, + "Pr@90": 89.66, + "IoU": 90.82 + }, + "bowl": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.5 + }, + "can": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 95.24, + "Pr@80": 95.24, + "Pr@90": 90.48, + "IoU": 96.44 + }, + "cap": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.4 + }, + "cereal": { + "Pr@50": 92.31, + "Pr@60": 92.31, + "Pr@70": 92.31, + "Pr@80": 92.31, + "Pr@90": 92.31, + "IoU": 93.11 + }, + "cup": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.36 + }, + "flashlight": { + "Pr@50": 93.75, + "Pr@60": 93.75, + "Pr@70": 93.75, + "Pr@80": 93.75, + "Pr@90": 93.75, + "IoU": 93.42 + }, + "fork": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 25.0, + "IoU": 64.43 + }, + "juice_box": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 88.89, + "IoU": 95.59 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.73 + }, + "knife": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 80.0, + "IoU": 92.02 + }, + "laptop": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 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{ + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 98.07 + }, + "pen": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 77.78, + "Pr@90": 33.33, + "IoU": 86.48 + }, + "plate": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.72 + }, + "pringles": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 90.0, + "IoU": 95.58 + }, + "scissors": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 94.27 + }, + "sponge": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.23 + }, + "spoon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 25.0, + "IoU": 71.72 + } +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_results.json b/my_projects/test_results/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_results.json new file mode 100644 index 0000000000..7848622188 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50.0": 96.53, + "mPr@60.0": 96.53, + "mPr@70.0": 96.14, + "mPr@80.0": 93.4, + "mPr@90.0": 84.5, + "mIoU": 91.34, + "average_fps": 21.17, + "fps_variance": 0.0277, + "average_mem": 549.5, + "mem_variance": 1.45 +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py b/my_projects/test_results/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..1e594caf51 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,529 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/test_results/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py b/my_projects/test_results/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..1e594caf51 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py @@ -0,0 +1,529 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=30, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 30 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/irl_vision_cat/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240925_173908/20240925_173908.json b/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240925_173908/20240925_173908.json new file mode 100644 index 0000000000..7718dacb33 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/20240925_173908/20240925_173908.json @@ -0,0 +1 @@ +{"mPr@50.0": 95.8, "mPr@60.0": 95.8, "mPr@70.0": 93.8, "mPr@80.0": 92.76, "mPr@90.0": 74.22, "mIoU": 88.01, "data_time": 0.7587986389795939, "time": 0.8079594089871361} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/benchmark/fps_20240925_174027.json b/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/benchmark/fps_20240925_174027.json new file mode 100644 index 0000000000..04951b676e --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/benchmark/fps_20240925_174027.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py", + "unit": "img / s", + "overall_fps_1": 62.65, + "overall_memory_1": 355, + "overall_fps_2": 62.63, + "overall_memory_2": 354, + 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b/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..5e888aedf1 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "fork", + "pred_label": "spoon", + "score": 17.554628038301008 + }, + { + "gt_label": "pringles", + "pred_label": "pen", + "score": 13.121327986570922 + }, + { + "gt_label": "knife", + "pred_label": "fork", + "score": 10.689388071262588 + }, + { + "gt_label": "knife", + "pred_label": "pen", + "score": 8.675445391169635 + }, + { + "gt_label": "spoon", + "pred_label": "knife", + "score": 8.56060606060606 + }, + { + "gt_label": "flashlight", + "pred_label": "marker", + "score": 5.045460366244078 + }, + { + "gt_label": "knife", + "pred_label": "spoon", + "score": 3.9504260263361735 + }, + { + "gt_label": "mouse", + "pred_label": "spoon", + "score": 3.66053841103086 + }, + { + "gt_label": "laptop", + "pred_label": "book", + "score": 3.614397096383923 + }, + { + "gt_label": "cap", + "pred_label": "plate", + "score": 3.4900471451021478 + } +] \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/flops/flops_20240925_174155.json b/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/flops/flops_20240925_174155.json new file mode 100644 index 0000000000..87ddef8178 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/flops/flops_20240925_174155.json @@ -0,0 +1,13 @@ +{ + "ori_shape": [ + 512, + 512 + ], + "pad_shape": [ + 512, + 512 + ], + "flops": "8.002G", + "params": "3.723M", + "compute_type": "direct: randomly generate a picture" +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_label_results.json b/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_label_results.json new file mode 100644 index 0000000000..f40a4d1559 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_label_results.json @@ -0,0 +1,242 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 99.65 + }, + "apple": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 94.86 + }, + "banana": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.23 + }, + "book": { + "Pr@50": 93.1, + "Pr@60": 93.1, + "Pr@70": 89.66, + "Pr@80": 89.66, + "Pr@90": 82.76, + "IoU": 86.92 + }, + "bowl": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.63 + }, + "can": { + "Pr@50": 95.24, + "Pr@60": 95.24, + "Pr@70": 95.24, + "Pr@80": 95.24, + "Pr@90": 90.48, + "IoU": 94.07 + }, + "cap": { + "Pr@50": 92.86, + "Pr@60": 92.86, + "Pr@70": 92.86, + "Pr@80": 92.86, + "Pr@90": 92.86, + "IoU": 92.26 + }, + "cereal": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 84.62, + "IoU": 93.76 + }, + "cup": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 95.45, + "IoU": 92.97 + }, + "flashlight": { + "Pr@50": 87.5, + "Pr@60": 87.5, + "Pr@70": 81.25, + "Pr@80": 81.25, + "Pr@90": 56.25, + "IoU": 88.08 + }, + "fork": { + "Pr@50": 75.0, + "Pr@60": 75.0, + "Pr@70": 75.0, + "Pr@80": 75.0, + "Pr@90": 50.0, + "IoU": 67.9 + }, + "juice_box": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 88.89, + "IoU": 94.42 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.48 + }, + "knife": { + "Pr@50": 60.0, + "Pr@60": 60.0, + "Pr@70": 60.0, + "Pr@80": 40.0, + "Pr@90": 20.0, + "IoU": 65.7 + }, + "laptop": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 85.71, + "Pr@80": 85.71, + "Pr@90": 85.71, + "IoU": 94.02 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 80.0, + "IoU": 92.24 + }, + "marker": { + "Pr@50": 92.86, + "Pr@60": 92.86, + "Pr@70": 92.86, + "Pr@80": 92.86, + "Pr@90": 57.14, + "IoU": 75.48 + }, + "milk": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 50.0, + "IoU": 89.04 + }, + "monitor": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 98.07 + }, + "mouse": { + "Pr@50": 87.5, + "Pr@60": 87.5, + "Pr@70": 87.5, + "Pr@80": 87.5, + "Pr@90": 25.0, + "IoU": 83.22 + }, + "mug": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 94.12, + "IoU": 94.81 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.77 + }, + "peach": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 83.33, + "IoU": 94.16 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.14 + }, + "pen": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 88.89, + "Pr@80": 77.78, + "Pr@90": 11.11, + "IoU": 56.93 + }, + "plate": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 92.27 + }, + "pringles": { + "Pr@50": 90.0, + "Pr@60": 90.0, + "Pr@70": 90.0, + "Pr@80": 90.0, + "Pr@90": 70.0, + "IoU": 80.16 + }, + "scissors": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 20.0, + "IoU": 89.2 + }, + "sponge": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 88.89, + "IoU": 93.3 + }, + "spoon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 75.0, + "Pr@80": 75.0, + "Pr@90": 0.0, + "IoU": 56.46 + } +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py b/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py new file mode 100644 index 0000000000..4734d7ee22 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512.py @@ -0,0 +1,328 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_results.json b/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_results.json new file mode 100644 index 0000000000..c6503f054b --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/segformer_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50.0": 95.8, + "mPr@60.0": 95.8, + "mPr@70.0": 93.8, + "mPr@80.0": 92.76, + "mPr@90.0": 74.22, + "mIoU": 88.01, + "average_fps": 62.24, + "fps_variance": 0.0727, + "average_mem": 354.1, + "mem_variance": 0.09 +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py b/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..4734d7ee22 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py @@ -0,0 +1,328 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=30, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/irl_vision_cat/segformer_mit-b0_1xb2-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240925_174201/20240925_174201.json b/my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240925_174201/20240925_174201.json new file mode 100644 index 0000000000..afe5719644 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/20240925_174201/20240925_174201.json @@ -0,0 +1 @@ +{"mPr@50.0": 96.82, "mPr@60.0": 95.01, "mPr@70.0": 93.8, "mPr@80.0": 90.46, "mPr@90.0": 79.82, "mIoU": 89.45, "data_time": 0.7622211433592296, "time": 0.8138373891512553} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/benchmark/fps_20240925_174322.json b/my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/benchmark/fps_20240925_174322.json new file mode 100644 index 0000000000..849f8f44c1 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/benchmark/fps_20240925_174322.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512.py", + "unit": "img / s", + "overall_fps_1": 52.07, + "overall_memory_1": 191, + "overall_fps_2": 52.05, + "overall_memory_2": 192, + "overall_fps_3": 51.96, + "overall_memory_3": 192, + "overall_fps_4": 51.76, + "overall_memory_4": 192, + "overall_fps_5": 51.77, + "overall_memory_5": 192, + "overall_fps_6": 51.81, + "overall_memory_6": 192, + "overall_fps_7": 51.65, + "overall_memory_7": 192, + "overall_fps_8": 51.61, + "overall_memory_8": 192, + "overall_fps_9": 51.56, + "overall_memory_9": 192, + "overall_fps_10": 51.49, + "overall_memory_10": 192, + "average_fps": 51.77, + "fps_variance": 0.0363, + "average_mem": 191.9, + "mem_variance": 0.09 +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/clutter/clutter_data.json b/my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/clutter/clutter_data.json new file mode 100644 index 0000000000..9e26dfeeb6 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/clutter/clutter_data.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json b/my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..cd4081a47c --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4352 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.0007235701829379753 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.001608393149502128 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 0.035148972143747015 + }, + { + "gt_label": 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+ ], + "flops": "6.325G", + "params": "4.233M", + "compute_type": "direct: randomly generate a picture" +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/segnext_label_results.json b/my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/segnext_label_results.json new file mode 100644 index 0000000000..cb30b5ab58 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/segnext_label_results.json @@ -0,0 +1,242 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 99.72 + }, + "apple": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.57 + }, + "banana": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 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'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=30, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/segnext_results.json b/my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/segnext_results.json new file mode 100644 index 0000000000..3df7c81cdc --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/segnext_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50.0": 96.82, + "mPr@60.0": 95.01, + "mPr@70.0": 93.8, + "mPr@80.0": 90.46, + "mPr@90.0": 79.82, + "mIoU": 89.45, + "average_fps": 51.77, + "fps_variance": 0.0363, + "average_mem": 191.9, + "mem_variance": 0.09 +} \ No newline at end of file diff --git a/my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py b/my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..ff3da363f7 --- /dev/null +++ b/my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show/vis_data/config.py @@ -0,0 +1,350 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/' +dataset_type = 'IRLVisionSimCATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=30, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/irl_vision_sim_cat/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='IRLVisionSimCATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/irl_vision_cat/segnext_mscan-t_1xb2-pre-ade20k-10k_irl_vision_sim_cat-512x512' diff --git a/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/20241027_225509/20241027_225509.json b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/20241027_225509/20241027_225509.json new file mode 100644 index 0000000000..009642f818 --- /dev/null +++ b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/20241027_225509/20241027_225509.json @@ -0,0 +1 @@ +{"mPr@50": 77.41, "mPr@60": 72.87, "mPr@70": 71.27, "mPr@80": 65.15, "mPr@90": 38.04, "mIoU": 68.89, "data_time": 0.7285064821657927, "time": 0.7658144261526025} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/20250209_122822/20250209_122822.json b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/20250209_122822/20250209_122822.json new file mode 100644 index 0000000000..d5ff9253a9 --- /dev/null +++ b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/20250209_122822/20250209_122822.json @@ -0,0 +1 @@ +{"mPr@50": 68.59, "mPr@60": 57.69, "mPr@70": 55.77, "mPr@80": 44.87, "mPr@90": 16.28, "mIoU": 61.39, "data_time": 0.6818634271621704, "time": 1.105500340461731} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/benchmark/fps_20241027_225631.json b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/benchmark/fps_20241027_225631.json new file mode 100644 index 0000000000..1200dfca3f --- /dev/null +++ b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/benchmark/fps_20241027_225631.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512.py", + "unit": "img / s", + "overall_fps_1": 101.81, + "overall_memory_1": 204, + "overall_fps_2": 102.59, + "overall_memory_2": 206, + "overall_fps_3": 102.37, + "overall_memory_3": 205, + "overall_fps_4": 101.91, + "overall_memory_4": 205, + "overall_fps_5": 102.08, + "overall_memory_5": 206, + "overall_fps_6": 101.94, + "overall_memory_6": 204, + "overall_fps_7": 101.9, + "overall_memory_7": 205, + "overall_fps_8": 101.98, + "overall_memory_8": 205, + "overall_fps_9": 102.09, + "overall_memory_9": 204, + "overall_fps_10": 102.01, + "overall_memory_10": 205, + "FPS": 102.07, + "Var FPS": 0.051, + "Mem (MB)": 204.9, + "Var Mem": 0.49 +} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/benchmark/fps_20250209_122844.json b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/benchmark/fps_20250209_122844.json new file mode 100644 index 0000000000..d46dcd4ea3 --- /dev/null +++ b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/benchmark/fps_20250209_122844.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512.py", + "unit": "img / s", + "overall_fps_1": 102.52, + "overall_memory_1": 204, + "overall_fps_2": 103.12, + "overall_memory_2": 206, + "overall_fps_3": 100.9, + "overall_memory_3": 205, + "overall_fps_4": 102.8, + "overall_memory_4": 205, + "overall_fps_5": 102.87, + "overall_memory_5": 206, + "overall_fps_6": 99.42, + "overall_memory_6": 204, + "overall_fps_7": 95.04, + "overall_memory_7": 205, + "overall_fps_8": 95.48, + "overall_memory_8": 205, + "overall_fps_9": 100.98, + "overall_memory_9": 204, + "overall_fps_10": 102.31, + "overall_memory_10": 205, + "FPS": 100.54, + "Var FPS": 8.1631, + "Mem (MB)": 204.9, + "Var Mem": 0.49 +} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/bisenetv1_label_results.json b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/bisenetv1_label_results.json new file mode 100644 index 0000000000..26ec291bd5 --- /dev/null +++ b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/bisenetv1_label_results.json @@ -0,0 +1,250 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 99.25 + }, + "apple": { + "Pr@50": 83.33, + "Pr@60": 83.33, + "Pr@70": 83.33, + "Pr@80": 83.33, + "Pr@90": 16.67, + "IoU": 88.36 + }, + "banana": { + "Pr@50": 100.0, + "Pr@60": 83.33, + "Pr@70": 83.33, + "Pr@80": 83.33, + "Pr@90": 50.0, + "IoU": 89.63 + }, + "book": { + "Pr@50": 93.75, + "Pr@60": 93.75, + "Pr@70": 93.75, + "Pr@80": 90.62, + "Pr@90": 68.75, + "IoU": 87.59 + }, + "bowl": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 78.79, + "IoU": 93.7 + }, + "can": { + "Pr@50": 96.0, + "Pr@60": 96.0, + "Pr@70": 96.0, + "Pr@80": 88.0, + "Pr@90": 44.0, + "IoU": 89.27 + }, + "cap": { + "Pr@50": 100.0, + "Pr@60": 92.86, + "Pr@70": 92.86, + "Pr@80": 92.86, + "Pr@90": 71.43, + "IoU": 90.71 + }, + "cereal": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 38.46, + "IoU": 86.81 + }, + "cup": { + "Pr@50": 96.0, + "Pr@60": 92.0, + "Pr@70": 92.0, + "Pr@80": 84.0, + "Pr@90": 16.0, + "IoU": 82.87 + }, + "flashlight": { + "Pr@50": 93.75, + "Pr@60": 87.5, + "Pr@70": 81.25, + "Pr@80": 62.5, + "Pr@90": 12.5, + "IoU": 81.28 + }, + "fork": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 9.83 + }, + "juice_box": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 50.0, + "IoU": 88.05 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 94.91 + }, + "knife": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + }, + "laptop": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 88.89, + "IoU": 94.02 + }, + "lemon": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 7.44 + }, + "marker": { + "Pr@50": 81.25, + "Pr@60": 81.25, + "Pr@70": 68.75, + "Pr@80": 56.25, + "Pr@90": 12.5, + "IoU": 52.31 + }, + "milk": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 66.67, + "Pr@90": 0.0, + "IoU": 82.42 + }, + "monitor": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 83.33, + "Pr@90": 83.33, + "IoU": 93.84 + }, + "mouse": { + "Pr@50": 50.0, + "Pr@60": 30.0, + "Pr@70": 20.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 44.81 + }, + "mug": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 94.12, + "Pr@90": 23.53, + "IoU": 87.14 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 80.0, + "Pr@90": 80.0, + "IoU": 92.83 + }, + "peach": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 42.86, + "IoU": 90.94 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 80.0, + "Pr@70": 80.0, + "Pr@80": 80.0, + "Pr@90": 60.0, + "IoU": 49.27 + }, + "pen": { + "Pr@50": 33.33, + "Pr@60": 8.33, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 25.72 + }, + "plate": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 86.67, + "IoU": 93.44 + }, + "pringles": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 85.71, + "Pr@90": 21.43, + "IoU": 86.59 + }, + "scissors": { + "Pr@50": 50.0, + "Pr@60": 25.0, + "Pr@70": 12.5, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 51.76 + }, + "sponge": { + "Pr@50": 88.89, + "Pr@60": 88.89, + "Pr@70": 88.89, + "Pr@80": 88.89, + "Pr@90": 33.33, + "IoU": 80.81 + }, + "spoon": { + "Pr@50": 33.33, + "Pr@60": 16.67, + "Pr@70": 16.67, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 20.08 + }, + "stapler": { + "Pr@50": 0.0, + "Pr@60": 0.0, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 0.0 + } +} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512.py b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512.py new file mode 100644 index 0000000000..afe2c013a9 --- /dev/null +++ b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512.py @@ -0,0 +1,342 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512' diff --git a/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/bisenetv1_results.json b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/bisenetv1_results.json new file mode 100644 index 0000000000..ddd813d563 --- /dev/null +++ b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/bisenetv1_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50": 68.59, + "mPr@60": 57.69, + "mPr@70": 55.77, + "mPr@80": 44.87, + "mPr@90": 16.28, + "mIoU": 61.39, + "FPS": 102.07, + "Var FPS": 0.051, + "Mem (MB)": 204.9, + "Var Mem": 0.49 +} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/clutter/clutter_data.json b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/clutter/clutter_data.json new file mode 100644 index 0000000000..9e26dfeeb6 --- /dev/null +++ b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/clutter/clutter_data.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/confusion_matrix/confusion.json b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..1d301e48d3 --- /dev/null +++ b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4652 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.005897666665483316 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.005321260408856791 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 0.049208298372003516 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.025285530092013907 + }, + { + "gt_label": "_background_", + "pred_label": "can", + "score": 0.020979660837214437 + }, + { + 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a/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/confusion_matrix/top_10_confusion.json b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..fb65cde1b5 --- /dev/null +++ b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "lemon", + "pred_label": "pear", + "score": 88.46489696122947 + }, + { + "gt_label": "stapler", + "pred_label": "mouse", + "score": 86.5926117282356 + }, + { + "gt_label": "knife", + "pred_label": "spoon", + "score": 38.22444106794009 + }, + { + "gt_label": "fork", + "pred_label": "spoon", + "score": 36.12238526381517 + }, + { + "gt_label": "spoon", + "pred_label": "marker", + "score": 17.745638200183656 + }, + { + "gt_label": "pen", + "pred_label": "marker", + "score": 17.16162943495401 + }, + { + "gt_label": "scissors", + "pred_label": "marker", + "score": 15.418525885705069 + }, + { + "gt_label": "knife", + "pred_label": "fork", + "score": 11.417408291729975 + }, + { + "gt_label": "fork", + "pred_label": "pen", + "score": 9.740867936309709 + }, + { + "gt_label": "knife", + "pred_label": "pen", + "score": 8.487084870848708 + } +] \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/flops/flops_20241027_225749.json b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/flops/flops_20241027_225749.json new file mode 100644 index 0000000000..1115d5291e --- /dev/null +++ b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/flops/flops_20241027_225749.json @@ -0,0 +1,13 @@ +{ + "ori_shape": [ + 512, + 512 + ], + "pad_shape": [ + 512, + 512 + ], + "flops": "14.851G", + "params": "13.279M", + "compute_type": "direct: randomly generate a picture" +} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/flops/flops_20250209_122924.json b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/flops/flops_20250209_122924.json new file mode 100644 index 0000000000..1115d5291e --- /dev/null +++ b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/flops/flops_20250209_122924.json @@ -0,0 +1,13 @@ +{ + "ori_shape": [ + 512, + 512 + ], + "pad_shape": [ + 512, + 512 + ], + "flops": "14.851G", + "params": "13.279M", + "compute_type": "direct: randomly generate a picture" +} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/show/vis_data/config.py b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..afe2c013a9 --- /dev/null +++ b/my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/show/vis_data/config.py @@ -0,0 +1,342 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/sodhots-c/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_sodhots-c-512x512' diff --git a/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_225756/20241027_225756.json b/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_225756/20241027_225756.json new file mode 100644 index 0000000000..7d14faad62 --- /dev/null +++ b/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_225756/20241027_225756.json @@ -0,0 +1 @@ +{"mPr@50": 94.93, "mPr@60": 94.66, "mPr@70": 93.39, "mPr@80": 91.31, "mPr@90": 78.37, "mIoU": 89.96, "data_time": 0.7335615417231685, "time": 0.83791853033978} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20250209_122931/20250209_122931.json b/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20250209_122931/20250209_122931.json new file mode 100644 index 0000000000..9491d2ba2a --- /dev/null +++ b/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/20250209_122931/20250209_122931.json @@ -0,0 +1 @@ +{"mPr@50": 94.87, "mPr@60": 94.87, "mPr@70": 85.9, "mPr@80": 78.85, "mPr@90": 48.72, "mIoU": 84.5, "data_time": 0.6988245844841003, "time": 1.017722338438034} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/benchmark/fps_20241027_225926.json b/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/benchmark/fps_20241027_225926.json new file mode 100644 index 0000000000..21fcd6f595 --- /dev/null +++ b/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/benchmark/fps_20241027_225926.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512.py", + "unit": "img / s", + "overall_fps_1": 15.84, + "overall_memory_1": 582, + "overall_fps_2": 15.53, + "overall_memory_2": 583, + "overall_fps_3": 15.71, + "overall_memory_3": 581, + "overall_fps_4": 15.65, + "overall_memory_4": 583, + "overall_fps_5": 15.63, + "overall_memory_5": 584, + "overall_fps_6": 15.59, + "overall_memory_6": 584, + "overall_fps_7": 15.55, + "overall_memory_7": 584, + "overall_fps_8": 15.54, + "overall_memory_8": 584, + "overall_fps_9": 15.54, + "overall_memory_9": 584, + "overall_fps_10": 15.53, + "overall_memory_10": 584, + "FPS": 15.61, + "Var FPS": 0.0089, + "Mem (MB)": 583.3, + "Var Mem": 1.01 +} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/benchmark/fps_20250209_122951.json b/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/benchmark/fps_20250209_122951.json new file mode 100644 index 0000000000..2b3b236615 --- /dev/null +++ b/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/benchmark/fps_20250209_122951.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512.py", + "unit": "img / s", + "overall_fps_1": 15.9, + "overall_memory_1": 582, + "overall_fps_2": 15.73, + "overall_memory_2": 583, + "overall_fps_3": 15.74, + "overall_memory_3": 581, + "overall_fps_4": 15.7, + "overall_memory_4": 583, + "overall_fps_5": 15.67, + "overall_memory_5": 584, + "overall_fps_6": 15.61, + "overall_memory_6": 584, + "overall_fps_7": 15.6, + "overall_memory_7": 584, + "overall_fps_8": 15.53, + "overall_memory_8": 584, + "overall_fps_9": 15.59, + 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"monitor", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "mouse", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "mug", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "orange", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "peach", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "pear", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "pen", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "plate", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "pringles", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "scissors", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "sponge", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "spoon", + "score": 0.0 + } +] \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/confusion_matrix/top_10_confusion.json b/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..5758c0bed5 --- /dev/null +++ b/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "knife", + "pred_label": "pen", + "score": 12.698068157152159 + }, + { + "gt_label": "fork", + "pred_label": "spoon", + "score": 10.099906337808305 + }, + { + "gt_label": "mouse", + "pred_label": "mug", + "score": 9.537037037037036 + }, + { + "gt_label": "spoon", + "pred_label": "scissors", + "score": 8.976124885215794 + }, + { + "gt_label": "knife", + "pred_label": "spoon", + "score": 8.552203169090514 + }, + { + "gt_label": "pringles", + "pred_label": "pen", + "score": 5.852390300843636 + }, + { + "gt_label": "marker", + "pred_label": "pringles", + "score": 5.564951715860112 + }, + { + "gt_label": "cap", + "pred_label": "laptop", + "score": 3.567530993539375 + }, + { + "gt_label": "banana", + "pred_label": "peach", + "score": 2.999592815454916 + }, + { + "gt_label": "marker", + "pred_label": "knife", + "score": 2.564242457308091 + } +] \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/mask2former_label_results.json b/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/mask2former_label_results.json new file mode 100644 index 0000000000..967e0f9bff --- /dev/null +++ b/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/mask2former_label_results.json @@ -0,0 +1,250 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 99.7 + }, + "apple": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 83.33, + "IoU": 96.96 + }, + "banana": { + "Pr@50": 83.33, + "Pr@60": 83.33, + "Pr@70": 83.33, + "Pr@80": 83.33, + "Pr@90": 83.33, + "IoU": 92.34 + }, + "book": { + "Pr@50": 93.75, + "Pr@60": 93.75, + "Pr@70": 93.75, + "Pr@80": 93.75, + "Pr@90": 93.75, + "IoU": 93.83 + }, + "bowl": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.31 + }, + "can": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.82 + }, + "cap": { + "Pr@50": 92.86, + "Pr@60": 92.86, + "Pr@70": 92.86, + "Pr@80": 92.86, + "Pr@90": 92.86, + "IoU": 94.07 + }, + "cereal": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 92.4 + }, + "cup": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 96.0, + "IoU": 95.56 + }, + "flashlight": { + "Pr@50": 87.5, + "Pr@60": 87.5, + "Pr@70": 87.5, + "Pr@80": 87.5, + "Pr@90": 87.5, + "IoU": 93.43 + }, + "fork": { + "Pr@50": 83.33, + "Pr@60": 83.33, + "Pr@70": 83.33, + "Pr@80": 66.67, + "Pr@90": 33.33, + "IoU": 77.23 + }, + "juice_box": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 92.86, + "IoU": 94.81 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.9 + }, + "knife": { + "Pr@50": 66.67, + "Pr@60": 66.67, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 16.67, + "IoU": 60.32 + }, + "laptop": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 93.76 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 83.33, + "Pr@90": 66.67, + "IoU": 93.82 + }, + "marker": { + "Pr@50": 87.5, + "Pr@60": 87.5, + "Pr@70": 87.5, + "Pr@80": 81.25, + "Pr@90": 68.75, + "IoU": 79.13 + }, + "milk": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 94.59 + }, + "monitor": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 98.09 + }, + "mouse": { + "Pr@50": 90.0, + "Pr@60": 90.0, + "Pr@70": 90.0, + "Pr@80": 90.0, + "Pr@90": 40.0, + "IoU": 80.2 + }, + "mug": { + "Pr@50": 94.12, + "Pr@60": 94.12, + "Pr@70": 94.12, + "Pr@80": 94.12, + "Pr@90": 94.12, + "IoU": 93.93 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 80.0, + "IoU": 97.37 + }, + "peach": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 85.71, + "Pr@80": 85.71, + "Pr@90": 85.71, + "IoU": 94.58 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 80.0, + "IoU": 97.36 + }, + "pen": { + "Pr@50": 100.0, + "Pr@60": 91.67, + "Pr@70": 83.33, + "Pr@80": 75.0, + "Pr@90": 25.0, + "IoU": 60.1 + }, + "plate": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.34 + }, + "pringles": { + "Pr@50": 92.86, + "Pr@60": 92.86, + "Pr@70": 92.86, + "Pr@80": 92.86, + "Pr@90": 92.86, + "IoU": 87.73 + }, + "scissors": { + "Pr@50": 87.5, + "Pr@60": 87.5, + "Pr@70": 87.5, + "Pr@80": 87.5, + "Pr@90": 50.0, + "IoU": 90.52 + }, + "sponge": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.34 + }, + "spoon": { + "Pr@50": 83.33, + "Pr@60": 83.33, + "Pr@70": 83.33, + "Pr@80": 66.67, + "Pr@90": 16.67, + "IoU": 62.93 + }, + "stapler": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 50.0, + "IoU": 91.17 + } +} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512.py b/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512.py new file mode 100644 index 0000000000..69fdffbe83 --- /dev/null +++ b/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512.py @@ -0,0 +1,440 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=31, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 31 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512' diff --git a/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/mask2former_results.json b/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/mask2former_results.json new file mode 100644 index 0000000000..33ee839b9e --- /dev/null +++ b/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/mask2former_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50": 94.87, + "mPr@60": 94.87, + "mPr@70": 85.9, + "mPr@80": 78.85, + "mPr@90": 48.72, + "mIoU": 84.5, + "FPS": 15.61, + "Var FPS": 0.0089, + "Mem (MB)": 583.3, + "Var Mem": 1.01 +} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/show/vis_data/config.py b/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..69fdffbe83 --- /dev/null +++ b/my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/show/vis_data/config.py @@ -0,0 +1,440 @@ +auto_scale_lr = dict(base_batch_size=16, enable=False) +crop_size = ( + 512, + 512, +) +custom_imports = dict(allow_failed_imports=False, imports='mmdet.models') +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + deep_stem=False, + depth=50, + frozen_stages=-1, + init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), + norm_cfg=dict(requires_grad=False, type='SyncBN'), + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + style='pytorch', + type='ResNet'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 256, + 512, + 1024, + 2048, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=2.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=5.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + loss_weight=5.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=True), + num_classes=31, + num_queries=100, + num_transformer_feat_level=3, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + encoder=dict( + init_cfg=None, + layer_cfg=dict( + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + embed_dims=256, + feedforward_channels=1024, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + batch_first=True, + dropout=0.0, + embed_dims=256, + im2col_step=64, + init_cfg=None, + norm_cfg=None, + num_heads=8, + num_levels=3, + num_points=4)), + num_layers=6), + init_cfg=None, + norm_cfg=dict(num_groups=32, type='GN'), + num_outs=3, + positional_encoding=dict(normalize=True, num_feats=128), + type='mmdet.MSDeformAttnPixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + strides=[ + 4, + 8, + 16, + 32, + ], + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=2.0), + dict( + type='mmdet.CrossEntropyLossCost', + use_sigmoid=True, + weight=5.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=5.0), + ], + type='mmdet.HungarianAssigner'), + importance_sample_ratio=0.75, + num_points=12544, + oversample_ratio=3.0, + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.0, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.0, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.0)), + num_layers=9, + return_intermediate=True), + type='Mask2FormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +num_classes = 31 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + backbone=dict(decay_mult=1.0, lr_mult=0.1), + level_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_embed=dict(decay_mult=0.0, lr_mult=1.0), + query_feat=dict(decay_mult=0.0, lr_mult=1.0)), + norm_decay_mult=0.0), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + eps=1e-08, + lr=0.0001, + type='AdamW', + weight_decay=0.05) +param_scheduler = [ + dict( + begin=0, + by_epoch=False, + end=10000, + eta_min=0, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/sodhots-c/mask2former_r50_1xb2-pre-ade20k-10k_sodhots-c-512x512' diff --git a/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_230237/20241027_230237.json b/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_230237/20241027_230237.json new file mode 100644 index 0000000000..c34e96aed2 --- /dev/null +++ b/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_230237/20241027_230237.json @@ -0,0 +1 @@ +{"mPr@50": 91.38, "mPr@60": 91.11, "mPr@70": 90.1, "mPr@80": 88.23, "mPr@90": 78.04, "mIoU": 85.66, "data_time": 0.732267791810243, "time": 0.8161080997923146} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20250209_123226/20250209_123226.json b/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20250209_123226/20250209_123226.json new file mode 100644 index 0000000000..829ab7b3c7 --- /dev/null +++ b/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/20250209_123226/20250209_123226.json @@ -0,0 +1 @@ +{"mPr@50": 89.1, "mPr@60": 89.1, "mPr@70": 83.97, "mPr@80": 78.85, "mPr@90": 55.45, "mIoU": 81.01, "data_time": 0.7576548159122467, "time": 1.0599492192268372} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/benchmark/fps_20241027_230406.json b/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/benchmark/fps_20241027_230406.json new file mode 100644 index 0000000000..6fe45c7ed8 --- /dev/null +++ b/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/benchmark/fps_20241027_230406.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512.py", + "unit": "img / s", + "overall_fps_1": 23.82, + "overall_memory_1": 550, + "overall_fps_2": 23.77, + "overall_memory_2": 551, + "overall_fps_3": 23.66, + "overall_memory_3": 549, + "overall_fps_4": 23.59, + "overall_memory_4": 549, + "overall_fps_5": 23.56, + "overall_memory_5": 549, + "overall_fps_6": 23.48, + "overall_memory_6": 549, + "overall_fps_7": 23.41, + "overall_memory_7": 549, + "overall_fps_8": 23.39, + "overall_memory_8": 549, + "overall_fps_9": 23.36, + "overall_memory_9": 549, + "overall_fps_10": 23.34, + "overall_memory_10": 549, + "FPS": 23.54, + "Var FPS": 0.026, + "Mem (MB)": 549.3, + "Var Mem": 0.41 +} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/benchmark/fps_20250209_123257.json b/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/benchmark/fps_20250209_123257.json new file mode 100644 index 0000000000..248e38bc7e --- /dev/null +++ b/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/benchmark/fps_20250209_123257.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512.py", + "unit": "img / s", + "overall_fps_1": 23.79, + "overall_memory_1": 550, + "overall_fps_2": 23.66, + "overall_memory_2": 551, + "overall_fps_3": 23.59, + "overall_memory_3": 549, + "overall_fps_4": 23.53, + "overall_memory_4": 549, + "overall_fps_5": 23.37, + "overall_memory_5": 549, + "overall_fps_6": 23.45, + "overall_memory_6": 549, + "overall_fps_7": 23.43, + "overall_memory_7": 549, + "overall_fps_8": 23.41, + "overall_memory_8": 549, + "overall_fps_9": 23.37, + "overall_memory_9": 549, + "overall_fps_10": 23.31, + "overall_memory_10": 549, + "FPS": 23.49, + "Var FPS": 0.0207, + "Mem (MB)": 549.3, + "Var Mem": 0.41 +} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/clutter/clutter_data.json b/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/clutter/clutter_data.json new file mode 100644 index 0000000000..9e26dfeeb6 --- /dev/null +++ b/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/clutter/clutter_data.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/confusion_matrix/confusion.json b/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..12e54c2b22 --- /dev/null +++ b/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/confusion_matrix/confusion.json @@ -0,0 +1,4652 @@ +[ + { + "gt_label": "_background_", + "pred_label": "apple", + "score": 0.001137543473342413 + }, + { + "gt_label": "_background_", + "pred_label": "banana", + "score": 0.0019582543685391206 + }, + { + "gt_label": "_background_", + "pred_label": "book", + "score": 0.027793469897335936 + }, + { + "gt_label": "_background_", + "pred_label": "bowl", + "score": 0.012623678746118656 + }, + { + "gt_label": "_background_", + "pred_label": "can", + "score": 0.007626885435362891 + }, + { + "gt_label": "_background_", 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"pred_label": "fork", + "score": 10.70110701107011 + }, + { + "gt_label": "pen", + "pred_label": "scissors", + "score": 6.59658344283837 + }, + { + "gt_label": "banana", + "pred_label": "pringles", + "score": 3.4113016332624526 + }, + { + "gt_label": "mouse", + "pred_label": "spoon", + "score": 2.6767676767676765 + }, + { + "gt_label": "knife", + "pred_label": "book", + "score": 2.257434339049273 + }, + { + "gt_label": "flashlight", + "pred_label": "marker", + "score": 2.037712895377129 + } +] \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/maskformer_label_results.json b/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/maskformer_label_results.json new file mode 100644 index 0000000000..54d10c49c2 --- /dev/null +++ b/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/maskformer_label_results.json @@ -0,0 +1,250 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 99.69 + }, + "apple": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 83.33, + "IoU": 96.94 + }, + "banana": { + "Pr@50": 83.33, + "Pr@60": 83.33, + "Pr@70": 83.33, + "Pr@80": 83.33, + "Pr@90": 83.33, + "IoU": 92.96 + }, + "book": { + "Pr@50": 96.88, + "Pr@60": 96.88, + "Pr@70": 96.88, + "Pr@80": 96.88, + "Pr@90": 93.75, + "IoU": 95.44 + }, + "bowl": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.49 + }, + "can": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.22 + }, + "cap": { + "Pr@50": 92.86, + "Pr@60": 92.86, + "Pr@70": 92.86, + "Pr@80": 92.86, + "Pr@90": 92.86, + "IoU": 88.43 + }, + "cereal": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 92.87 + }, + "cup": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 96.0, + "IoU": 95.13 + }, + "flashlight": { + "Pr@50": 93.75, + "Pr@60": 93.75, + "Pr@70": 93.75, + "Pr@80": 93.75, + "Pr@90": 93.75, + "IoU": 93.53 + }, + "fork": { + "Pr@50": 16.67, + "Pr@60": 16.67, + "Pr@70": 0.0, + "Pr@80": 0.0, + "Pr@90": 0.0, + "IoU": 20.52 + }, + "juice_box": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 92.86, + "IoU": 94.32 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.2 + }, + "knife": { + "Pr@50": 16.67, + "Pr@60": 16.67, + "Pr@70": 16.67, + "Pr@80": 16.67, + "Pr@90": 16.67, + "IoU": 13.51 + }, + "laptop": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.47 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 83.33, + "Pr@90": 83.33, + "IoU": 94.81 + }, + "marker": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 93.75, + "Pr@80": 87.5, + "Pr@90": 68.75, + "IoU": 84.86 + }, + "milk": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 92.06 + }, + "monitor": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.77 + }, + "mouse": { + "Pr@50": 90.0, + "Pr@60": 90.0, + "Pr@70": 90.0, + "Pr@80": 80.0, + "Pr@90": 40.0, + "IoU": 87.24 + }, + "mug": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.57 + }, + "orange": { + "Pr@50": 80.0, + "Pr@60": 80.0, + "Pr@70": 80.0, + "Pr@80": 80.0, + "Pr@90": 60.0, + "IoU": 66.05 + }, + "peach": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.99 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 80.0, + "IoU": 97.59 + }, + "pen": { + "Pr@50": 91.67, + "Pr@60": 83.33, + "Pr@70": 75.0, + "Pr@80": 66.67, + "Pr@90": 41.67, + "IoU": 65.5 + }, + "plate": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 97.5 + }, + "pringles": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 92.86, + "IoU": 93.87 + }, + "scissors": { + "Pr@50": 87.5, + "Pr@60": 87.5, + "Pr@70": 87.5, + "Pr@80": 87.5, + "Pr@90": 50.0, + "IoU": 86.92 + }, + "sponge": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 95.52 + }, + "spoon": { + "Pr@50": 83.33, + "Pr@60": 83.33, + "Pr@70": 83.33, + "Pr@80": 66.67, + "Pr@90": 0.0, + "IoU": 39.27 + }, + "stapler": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 50.0, + "IoU": 91.1 + } +} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/maskformer_results.json b/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/maskformer_results.json new file mode 100644 index 0000000000..c8ae2cc1fa --- /dev/null +++ b/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/maskformer_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50": 89.1, + "mPr@60": 89.1, + "mPr@70": 83.97, + "mPr@80": 78.85, + "mPr@90": 55.45, + "mIoU": 81.01, + "FPS": 23.54, + "Var FPS": 0.026, + "Mem (MB)": 549.3, + "Var Mem": 0.41 +} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512.py b/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512.py new file mode 100644 index 0000000000..41cf7f9883 --- /dev/null +++ b/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512.py @@ -0,0 +1,530 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=31, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 31 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512' diff --git a/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/show/vis_data/config.py b/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..41cf7f9883 --- /dev/null +++ b/my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/show/vis_data/config.py @@ -0,0 +1,530 @@ +backbone_embed_multi = dict(decay_mult=0.0, lr_mult=1.0) +backbone_norm_cfg = dict(requires_grad=True, type='LN') +backbone_norm_multi = dict(decay_mult=0.0, lr_mult=1.0) +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth' +crop_size = ( + 512, + 512, +) +custom_keys = dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) +}) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=5000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +depths = [ + 2, + 2, + 6, + 2, +] +embed_multi = dict(decay_mult=0.0) +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attn_drop_rate=0.0, + depths=[ + 2, + 2, + 6, + 2, + ], + drop_path_rate=0.3, + drop_rate=0.0, + embed_dims=96, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_tiny_patch4_window7_224_20220317-1cdeb081.pth', + type='Pretrained'), + mlp_ratio=4, + norm_cfg=dict(requires_grad=True, type='LN'), + num_heads=[ + 3, + 6, + 12, + 24, + ], + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_norm=True, + patch_size=4, + pretrain_img_size=( + 640, + 480, + ), + qk_scale=None, + qkv_bias=True, + strides=( + 4, + 2, + 2, + 2, + ), + type='SwinTransformer', + use_abs_pos_embed=False, + window_size=7), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + enforce_decoder_input_project=False, + feat_channels=256, + in_channels=[ + 96, + 192, + 384, + 768, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_cls=dict( + class_weight=[ + 0.1, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + 1.0, + ], + loss_weight=1.0, + reduction='mean', + type='mmdet.CrossEntropyLoss', + use_sigmoid=False), + loss_dice=dict( + activate=True, + eps=1.0, + loss_weight=1.0, + naive_dice=True, + reduction='mean', + type='mmdet.DiceLoss', + use_sigmoid=True), + loss_mask=dict( + alpha=0.25, + gamma=2.0, + loss_weight=20.0, + reduction='mean', + type='mmdet.FocalLoss', + use_sigmoid=True), + num_classes=31, + num_queries=100, + out_channels=256, + pixel_decoder=dict( + act_cfg=dict(type='ReLU'), + norm_cfg=dict(num_groups=32, type='GN'), + type='mmdet.PixelDecoder'), + positional_encoding=dict(normalize=True, num_feats=128), + train_cfg=dict( + assigner=dict( + match_costs=[ + dict(type='mmdet.ClassificationCost', weight=1.0), + dict( + binary_input=True, + type='mmdet.FocalLossCost', + weight=20.0), + dict( + eps=1.0, + pred_act=True, + type='mmdet.DiceCost', + weight=1.0), + ], + type='mmdet.HungarianAssigner'), + sampler=dict(type='mmdet.MaskPseudoSampler')), + transformer_decoder=dict( + init_cfg=None, + layer_cfg=dict( + cross_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1), + ffn_cfg=dict( + act_cfg=dict(inplace=True, type='ReLU'), + add_identity=True, + dropout_layer=None, + embed_dims=256, + feedforward_channels=2048, + ffn_drop=0.1, + num_fcs=2), + self_attn_cfg=dict( + attn_drop=0.1, + batch_first=True, + dropout_layer=None, + embed_dims=256, + num_heads=8, + proj_drop=0.1)), + num_layers=6, + return_intermediate=True), + type='MaskFormerHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +num_classes = 31 +optim_wrapper = dict( + clip_grad=dict(max_norm=0.01, norm_type=2), + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict({ + 'backbone': + dict(lr_mult=1.0), + 'backbone.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.patch_embed.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.0.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.1.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.2.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.3.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.4.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.blocks.5.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.2.downsample.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.0.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'backbone.stages.3.blocks.1.norm': + dict(decay_mult=0.0, lr_mult=1.0), + 'query_embed': + dict(decay_mult=0.0), + 'relative_position_bias_table': + dict(decay_mult=0.0, lr_mult=1.0) + })), + type='OptimWrapper') +optimizer = dict( + betas=( + 0.9, + 0.999, + ), + lr=6e-05, + momentum=0.9, + type='AdamW', + weight_decay=0.01) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=5000, start_factor=1e-06, + type='LinearLR'), + dict( + begin=5000, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/sodhots-c/maskformer_swin-t_upernet_1xb2-pre-ade20k-10k_sodhots-c-512x512' diff --git a/my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/20241027_230633/20241027_230633.json b/my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/20241027_230633/20241027_230633.json new file mode 100644 index 0000000000..f12038101e --- /dev/null +++ b/my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/20241027_230633/20241027_230633.json @@ -0,0 +1 @@ +{"mPr@50": 91.16, "mPr@60": 88.65, "mPr@70": 86.64, "mPr@80": 81.11, "mPr@90": 62.86, "mIoU": 83.81, "data_time": 0.7304331116054369, "time": 0.7780139213022979} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/20250209_123449/20250209_123449.json b/my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/20250209_123449/20250209_123449.json new file mode 100644 index 0000000000..26cb9dd414 --- /dev/null +++ b/my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/20250209_123449/20250209_123449.json @@ -0,0 +1 @@ +{"mPr@50": 80.13, "mPr@60": 75.0, "mPr@70": 65.38, "mPr@80": 52.56, "mPr@90": 24.1, "mIoU": 72.11, "data_time": 0.6940692067146301, "time": 0.9589154124259949} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/benchmark/fps_20241027_230756.json b/my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/benchmark/fps_20241027_230756.json new file mode 100644 index 0000000000..01705e1f6c --- /dev/null +++ b/my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/benchmark/fps_20241027_230756.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/segformer_mit-b0_1xb2-10k_sodhots-c-512x512.py", + "unit": "img / s", + "overall_fps_1": 67.86, + "overall_memory_1": 355, 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"stapler": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 50.0, + "Pr@90": 0.0, + "IoU": 81.02 + } +} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/segformer_mit-b0_1xb2-10k_sodhots-c-512x512.py b/my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/segformer_mit-b0_1xb2-10k_sodhots-c-512x512.py new file mode 100644 index 0000000000..86b7ccbe3c --- /dev/null +++ b/my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/segformer_mit-b0_1xb2-10k_sodhots-c-512x512.py @@ -0,0 +1,328 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512' diff --git a/my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/segformer_results.json b/my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/segformer_results.json new file mode 100644 index 0000000000..8215d616b1 --- /dev/null +++ b/my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/segformer_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50": 80.13, + "mPr@60": 75.0, + "mPr@70": 65.38, + "mPr@80": 52.56, + "mPr@90": 24.1, + "mIoU": 72.11, + "FPS": 68.11, + "Var FPS": 0.0395, + "Mem (MB)": 354.3, + "Var Mem": 0.21 +} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/show/vis_data/config.py b/my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..86b7ccbe3c --- /dev/null +++ b/my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/show/vis_data/config.py @@ -0,0 +1,328 @@ +checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + attn_drop_rate=0.0, + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=32, + in_channels=3, + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth', + type='Pretrained'), + mlp_ratio=4, + num_heads=[ + 1, + 2, + 5, + 8, + ], + num_layers=[ + 2, + 2, + 2, + 2, + ], + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + patch_sizes=[ + 7, + 3, + 3, + 3, + ], + qkv_bias=True, + sr_ratios=[ + 8, + 4, + 2, + 1, + ], + type='MixVisionTransformer'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + in_channels=[ + 32, + 64, + 160, + 256, + ], + in_index=[ + 0, + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=31, + type='SegformerHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/sodhots-c/segformer_mit-b0_1xb2-10k_sodhots-c-512x512' diff --git a/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_230926/20241027_230926.json b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_230926/20241027_230926.json new file mode 100644 index 0000000000..832deba142 --- /dev/null +++ b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20241027_230926/20241027_230926.json @@ -0,0 +1 @@ +{"mPr@50": 93.12, "mPr@60": 91.28, "mPr@70": 87.46, "mPr@80": 82.98, "mPr@90": 70.17, "mIoU": 86.31, "data_time": 0.7290700570396755, "time": 0.7780195681945138} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20250209_123603/20250209_123603.json b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20250209_123603/20250209_123603.json new file mode 100644 index 0000000000..69fb72bc22 --- /dev/null +++ b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/20250209_123603/20250209_123603.json @@ -0,0 +1 @@ +{"mPr@50": 85.26, "mPr@60": 79.49, "mPr@70": 73.72, "mPr@80": 68.27, "mPr@90": 46.03, "mIoU": 69.05, "data_time": 0.6795405447483063, "time": 0.9271383583545685} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/benchmark/fps_20241027_231050.json b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/benchmark/fps_20241027_231050.json new file mode 100644 index 0000000000..4d9de4bb3e --- /dev/null +++ b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/benchmark/fps_20241027_231050.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512.py", + "unit": "img / s", + "overall_fps_1": 56.87, + "overall_memory_1": 191, + "overall_fps_2": 56.93, + "overall_memory_2": 192, + "overall_fps_3": 56.97, + "overall_memory_3": 192, + "overall_fps_4": 56.7, + "overall_memory_4": 192, + "overall_fps_5": 56.68, + "overall_memory_5": 192, + "overall_fps_6": 56.69, + "overall_memory_6": 192, + "overall_fps_7": 56.68, + "overall_memory_7": 192, + "overall_fps_8": 56.45, + "overall_memory_8": 192, + "overall_fps_9": 56.41, + "overall_memory_9": 192, + "overall_fps_10": 56.29, + "overall_memory_10": 192, + "FPS": 56.67, + "Var FPS": 0.046, + "Mem (MB)": 191.9, + "Var Mem": 0.09 +} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/benchmark/fps_20250209_123632.json b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/benchmark/fps_20250209_123632.json new file mode 100644 index 0000000000..7f24a345c1 --- /dev/null +++ b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/benchmark/fps_20250209_123632.json @@ -0,0 +1,28 @@ +{ + "config": "my_projects/best_models/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512.py", + "unit": "img / s", + "overall_fps_1": 56.96, + "overall_memory_1": 191, + "overall_fps_2": 56.8, + "overall_memory_2": 192, + "overall_fps_3": 56.84, + "overall_memory_3": 192, + "overall_fps_4": 56.7, + "overall_memory_4": 192, + "overall_fps_5": 56.53, + "overall_memory_5": 192, + "overall_fps_6": 56.52, + "overall_memory_6": 192, + "overall_fps_7": 56.52, + "overall_memory_7": 192, + "overall_fps_8": 56.52, + "overall_memory_8": 192, + "overall_fps_9": 56.51, + "overall_memory_9": 192, + "overall_fps_10": 56.56, + "overall_memory_10": 192, + "FPS": 56.65, + "Var FPS": 0.0248, + "Mem (MB)": 191.9, + "Var Mem": 0.09 +} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/clutter/clutter_data.json b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/clutter/clutter_data.json new file mode 100644 index 0000000000..9e26dfeeb6 --- /dev/null +++ b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/clutter/clutter_data.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/confusion_matrix/confusion.json b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/confusion_matrix/confusion.json new file mode 100644 index 0000000000..1bc215300a --- /dev/null +++ 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0.0 + }, + { + "gt_label": "stapler", + "pred_label": "scissors", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "sponge", + "score": 0.0 + }, + { + "gt_label": "stapler", + "pred_label": "spoon", + "score": 0.0 + } +] \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/confusion_matrix/top_10_confusion.json b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/confusion_matrix/top_10_confusion.json new file mode 100644 index 0000000000..848450f51a --- /dev/null +++ b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/confusion_matrix/top_10_confusion.json @@ -0,0 +1,52 @@ +[ + { + "gt_label": "fork", + "pred_label": "spoon", + "score": 27.739619107087105 + }, + { + "gt_label": "knife", + "pred_label": "fork", + "score": 14.000434121988278 + }, + { + "gt_label": "knife", + "pred_label": "pen", + "score": 13.76166702843499 + }, + { + "gt_label": "knife", + "pred_label": "spoon", + "score": 13.501193835467767 + }, + { + "gt_label": "pringles", + "pred_label": "pen", + "score": 5.678622592455032 + }, + { + "gt_label": "knife", + "pred_label": "marker", + "score": 5.209463859344476 + }, + { + "gt_label": "milk", + "pred_label": "juice_box", + "score": 4.699719079796523 + }, + { + "gt_label": "fork", + "pred_label": "knife", + "score": 3.699656571963784 + }, + { + "gt_label": "laptop", + "pred_label": "cereal", + "score": 2.881529071968224 + }, + { + "gt_label": "pen", + "pred_label": "scissors", + "score": 2.724485326325011 + } +] \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/flops/flops_20241027_231219.json b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/flops/flops_20241027_231219.json new file mode 100644 index 0000000000..76060a5c64 --- /dev/null +++ b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/flops/flops_20241027_231219.json @@ -0,0 +1,13 @@ +{ + "ori_shape": [ + 512, + 512 + ], + "pad_shape": [ + 512, + 512 + ], + "flops": "6.326G", + "params": "4.233M", + "compute_type": "direct: randomly generate a picture" +} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/flops/flops_20250209_123726.json b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/flops/flops_20250209_123726.json new file mode 100644 index 0000000000..76060a5c64 --- /dev/null +++ b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/flops/flops_20250209_123726.json @@ -0,0 +1,13 @@ +{ + "ori_shape": [ + 512, + 512 + ], + "pad_shape": [ + 512, + 512 + ], + "flops": "6.326G", + "params": "4.233M", + "compute_type": "direct: randomly generate a picture" +} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/segnext_label_results.json b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/segnext_label_results.json new file mode 100644 index 0000000000..06fe9474e1 --- /dev/null +++ b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/segnext_label_results.json @@ -0,0 +1,250 @@ +{ + "_background_": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 99.61 + }, + "apple": { + "Pr@50": 83.33, + "Pr@60": 83.33, + "Pr@70": 83.33, + "Pr@80": 83.33, + "Pr@90": 83.33, + "IoU": 94.02 + }, + "banana": { + "Pr@50": 83.33, + "Pr@60": 83.33, + "Pr@70": 83.33, + "Pr@80": 83.33, + "Pr@90": 83.33, + "IoU": 92.16 + }, + "book": { + "Pr@50": 93.75, + "Pr@60": 93.75, + "Pr@70": 90.62, + "Pr@80": 84.38, + "Pr@90": 78.12, + "IoU": 88.39 + }, + "bowl": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.6 + }, + "can": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 96.0, + "Pr@90": 92.0, + "IoU": 94.32 + }, + "cap": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 92.86, + "Pr@90": 92.86, + "IoU": 95.61 + }, + "cereal": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 92.31, + "IoU": 89.58 + }, + "cup": { + "Pr@50": 100.0, + "Pr@60": 96.0, + "Pr@70": 96.0, + "Pr@80": 96.0, + "Pr@90": 92.0, + "IoU": 94.47 + }, + "flashlight": { + "Pr@50": 93.75, + "Pr@60": 93.75, + "Pr@70": 93.75, + "Pr@80": 87.5, + "Pr@90": 87.5, + "IoU": 92.85 + }, + "fork": { + "Pr@50": 50.0, + "Pr@60": 50.0, + "Pr@70": 16.67, + "Pr@80": 16.67, + "Pr@90": 0.0, + "IoU": 46.75 + }, + "juice_box": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 78.57, + "IoU": 92.76 + }, + "keyboard": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 87.5, + "IoU": 96.0 + }, + "knife": { + "Pr@50": 16.67, + "Pr@60": 16.67, + "Pr@70": 16.67, + "Pr@80": 16.67, + "Pr@90": 16.67, + "IoU": 36.47 + }, + "laptop": { + "Pr@50": 100.0, + "Pr@60": 88.89, + "Pr@70": 88.89, + "Pr@80": 88.89, + "Pr@90": 88.89, + "IoU": 94.14 + }, + "lemon": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 83.33, + "Pr@90": 66.67, + "IoU": 93.61 + }, + "marker": { + "Pr@50": 93.75, + "Pr@60": 93.75, + "Pr@70": 87.5, + "Pr@80": 81.25, + "Pr@90": 56.25, + "IoU": 81.01 + }, + "milk": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 83.33, + "Pr@90": 66.67, + "IoU": 80.42 + }, + "monitor": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.96 + }, + "mouse": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 70.0, + "Pr@80": 40.0, + "Pr@90": 10.0, + "IoU": 83.83 + }, + "mug": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.11 + }, + "orange": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 80.0, + "IoU": 97.31 + }, + "peach": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 96.28 + }, + "pear": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 80.0, + "IoU": 96.35 + }, + "pen": { + "Pr@50": 91.67, + "Pr@60": 66.67, + "Pr@70": 66.67, + "Pr@80": 33.33, + "Pr@90": 33.33, + "IoU": 53.78 + }, + "plate": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 93.33, + "IoU": 93.56 + }, + "pringles": { + "Pr@50": 92.86, + "Pr@60": 92.86, + "Pr@70": 92.86, + "Pr@80": 92.86, + "Pr@90": 78.57, + "IoU": 87.13 + }, + "scissors": { + "Pr@50": 87.5, + "Pr@60": 87.5, + "Pr@70": 75.0, + "Pr@80": 62.5, + "Pr@90": 37.5, + "IoU": 86.0 + }, + "sponge": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 100.0, + "IoU": 94.14 + }, + "spoon": { + "Pr@50": 100.0, + "Pr@60": 83.33, + "Pr@70": 50.0, + "Pr@80": 50.0, + "Pr@90": 0.0, + "IoU": 46.25 + }, + "stapler": { + "Pr@50": 100.0, + "Pr@60": 100.0, + "Pr@70": 100.0, + "Pr@80": 100.0, + "Pr@90": 0.0, + "IoU": 89.05 + } +} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512.py b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512.py new file mode 100644 index 0000000000..727ba67c6f --- /dev/null +++ b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512.py @@ -0,0 +1,350 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=31, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512' diff --git a/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/segnext_results.json b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/segnext_results.json new file mode 100644 index 0000000000..ec589e88c4 --- /dev/null +++ b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/segnext_results.json @@ -0,0 +1,12 @@ +{ + "mPr@50": 85.26, + "mPr@60": 79.49, + "mPr@70": 73.72, + "mPr@80": 68.27, + "mPr@90": 46.03, + "mIoU": 69.05, + "FPS": 56.67, + "Var FPS": 0.046, + "Mem (MB)": 191.9, + "Var Mem": 0.09 +} \ No newline at end of file diff --git a/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/show/vis_data/config.py b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/show/vis_data/config.py new file mode 100644 index 0000000000..727ba67c6f --- /dev/null +++ b/my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/show/vis_data/config.py @@ -0,0 +1,350 @@ +checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth' +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/' +dataset_type = 'SODHOTSCDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +ham_norm_cfg = dict(num_groups=32, requires_grad=True, type='GN') +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/best_models/selection_trained/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + backbone=dict( + act_cfg=dict(type='GELU'), + attention_kernel_paddings=[ + 2, + [ + 0, + 3, + ], + [ + 0, + 5, + ], + [ + 0, + 10, + ], + ], + attention_kernel_sizes=[ + 5, + [ + 1, + 7, + ], + [ + 1, + 11, + ], + [ + 1, + 21, + ], + ], + depths=[ + 3, + 3, + 5, + 2, + ], + drop_path_rate=0.1, + drop_rate=0.0, + embed_dims=[ + 32, + 64, + 160, + 256, + ], + init_cfg=dict( + checkpoint= + 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth', + type='Pretrained'), + mlp_ratios=[ + 8, + 8, + 4, + 4, + ], + norm_cfg=dict(requires_grad=True, type='BN'), + type='MSCAN'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + test_cfg=dict(size_divisor=32), + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + dropout_ratio=0.1, + ham_channels=256, + ham_kwargs=dict( + MD_R=16, + MD_S=1, + eval_steps=7, + inv_t=100, + rand_init=True, + train_steps=6), + in_channels=[ + 64, + 160, + 256, + ], + in_index=[ + 1, + 2, + 3, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(num_groups=32, requires_grad=True, type='GN'), + num_classes=31, + type='LightHamHead'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=6e-05, type='AdamW', weight_decay=0.01), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0, + power=1.0, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/test', seg_map_path='ann_dir/test'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + keep_results=True, + output_dir= + 'my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/pred_results', + type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root= + '/media/ids/Ubuntu files/data/SODHOTS-C/SemanticSegmentation/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='SODHOTSCDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir= + 'my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512/show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = 'my_projects/test_results/sodhots-c/segnext_mscan-t_1xb2-pre-ade20k-10k_sodhots-c-512x512' diff --git a/setup.py b/setup.py deleted file mode 100755 index 45d923db60..0000000000 --- a/setup.py +++ /dev/null @@ -1,200 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import os -import os.path as osp -import platform -import shutil -import sys -import warnings -from setuptools import find_packages, setup - - -def readme(): - with open('README.md', encoding='utf-8') as f: - content = f.read() - return content - - -version_file = 'mmseg/version.py' - - -def get_version(): - with open(version_file) as f: - exec(compile(f.read(), version_file, 'exec')) - return locals()['__version__'] - - -def parse_requirements(fname='requirements.txt', with_version=True): - """Parse the package dependencies listed in a requirements file but strips - specific versioning information. - - Args: - fname (str): path to requirements file - with_version (bool, default=False): if True include version specs - - Returns: - List[str]: list of requirements items - - CommandLine: - python -c "import setup; print(setup.parse_requirements())" - """ - import re - import sys - from os.path import exists - require_fpath = fname - - def parse_line(line): - """Parse information from a line in a requirements text file.""" - if line.startswith('-r '): - # Allow specifying requirements in other files - target = line.split(' ')[1] - for info in parse_require_file(target): - yield info - else: - info = {'line': line} - if line.startswith('-e '): - info['package'] = line.split('#egg=')[1] - else: - # Remove versioning from the package - pat = '(' + '|'.join(['>=', '==', '>']) + ')' - parts = re.split(pat, line, maxsplit=1) - parts = [p.strip() for p in parts] - - info['package'] = parts[0] - if len(parts) > 1: - op, rest = parts[1:] - if ';' in rest: - # Handle platform specific dependencies - # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies - version, platform_deps = map(str.strip, - rest.split(';')) - info['platform_deps'] = platform_deps - else: - version = rest # NOQA - info['version'] = (op, version) - yield info - - def parse_require_file(fpath): - with open(fpath) as f: - for line in f.readlines(): - line = line.strip() - if line and not line.startswith('#'): - yield from parse_line(line) - - def gen_packages_items(): - if exists(require_fpath): - for info in parse_require_file(require_fpath): - parts = [info['package']] - if with_version and 'version' in info: - parts.extend(info['version']) - if not sys.version.startswith('3.4'): - # apparently package_deps are broken in 3.4 - platform_deps = info.get('platform_deps') - if platform_deps is not None: - parts.append(';' + platform_deps) - item = ''.join(parts) - yield item - - packages = list(gen_packages_items()) - return packages - - -def add_mim_extension(): - """Add extra files that are required to support MIM into the package. - - These files will be added by creating a symlink to the originals if the - package is installed in `editable` mode (e.g. pip install -e .), or by - copying from the originals otherwise. - """ - - # parse installment mode - if 'develop' in sys.argv: - # installed by `pip install -e .` - if platform.system() == 'Windows': - # set `copy` mode here since symlink fails on Windows. - mode = 'copy' - else: - mode = 'symlink' - elif 'sdist' in sys.argv or 'bdist_wheel' in sys.argv or \ - platform.system() == 'Windows': - # installed by `pip install .` - # or create source distribution by `python setup.py sdist` - # set `copy` mode here since symlink fails with WinError on Windows. - mode = 'copy' - else: - return - - filenames = ['tools', 'configs', 'model-index.yml', 'dataset-index.yml'] - repo_path = osp.dirname(__file__) - mim_path = osp.join(repo_path, 'mmseg', '.mim') - os.makedirs(mim_path, exist_ok=True) - - for filename in filenames: - if osp.exists(filename): - src_path = osp.join(repo_path, filename) - tar_path = osp.join(mim_path, filename) - - if osp.isfile(tar_path) or osp.islink(tar_path): - os.remove(tar_path) - elif osp.isdir(tar_path): - shutil.rmtree(tar_path) - - if mode == 'symlink': - src_relpath = osp.relpath(src_path, osp.dirname(tar_path)) - try: - os.symlink(src_relpath, tar_path) - except OSError: - # Creating a symbolic link on windows may raise an - # `OSError: [WinError 1314]` due to privilege. If - # the error happens, the src file will be copied - mode = 'copy' - warnings.warn( - f'Failed to create a symbolic link for {src_relpath}, ' - f'and it will be copied to {tar_path}') - else: - continue - - if mode == 'copy': - if osp.isfile(src_path): - shutil.copyfile(src_path, tar_path) - elif osp.isdir(src_path): - shutil.copytree(src_path, tar_path) - else: - warnings.warn(f'Cannot copy file {src_path}.') - else: - raise ValueError(f'Invalid mode {mode}') - - -if __name__ == '__main__': - add_mim_extension() - setup( - name='mmsegmentation', - version=get_version(), - description='Open MMLab Semantic Segmentation Toolbox and Benchmark', - long_description=readme(), - long_description_content_type='text/markdown', - author='MMSegmentation Contributors', - author_email='openmmlab@gmail.com', - keywords='computer vision, semantic segmentation', - url='https://github.com/open-mmlab/mmsegmentation', - packages=find_packages(exclude=('configs', 'tools', 'demo')), - include_package_data=True, - classifiers=[ - 'Development Status :: 4 - Beta', - 'License :: OSI Approved :: Apache Software License', - 'Operating System :: OS Independent', - 'Programming Language :: Python :: 3.6', - 'Programming Language :: Python :: 3.7', - 'Programming Language :: Python :: 3.8', - 'Programming Language :: Python :: 3.9', - ], - license='Apache License 2.0', - install_requires=parse_requirements('requirements/runtime.txt'), - extras_require={ - 'all': parse_requirements('requirements.txt'), - 'tests': parse_requirements('requirements/tests.txt'), - 'optional': parse_requirements('requirements/optional.txt'), - 'mim': parse_requirements('requirements/mminstall.txt'), - 'multimodal': parse_requirements('requirements/multimodal.txt'), - }, - ext_modules=[], - zip_safe=False) diff --git a/show/vis_data/config.py b/show/vis_data/config.py new file mode 100644 index 0000000000..9422828c9b --- /dev/null +++ b/show/vis_data/config.py @@ -0,0 +1,334 @@ +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = '/media/ids/Ubuntu files/data/ARID20_CAT/' +dataset_type = 'ARID20CATDataset' +default_hooks = dict( + checkpoint=dict( + by_epoch=False, interval=1000, save_best='mIoU', + type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(draw=True, type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'none' +load_from = 'my_projects/tests/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512/weights.pth' +log_level = 'INFO' +log_processor = dict(by_epoch=False) +model = dict( + auxiliary_head=[ + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=1, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + dict( + align_corners=False, + channels=64, + concat_input=False, + in_channels=128, + in_index=2, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + ], + backbone=dict( + align_corners=False, + backbone_cfg=dict( + contract_dilation=True, + depth=18, + dilations=( + 1, + 1, + 1, + 1, + ), + in_channels=3, + init_cfg=dict( + checkpoint='open-mmlab://resnet18_v1c', type='Pretrained'), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + norm_eval=False, + num_stages=4, + out_indices=( + 0, + 1, + 2, + 3, + ), + strides=( + 1, + 2, + 2, + 2, + ), + style='pytorch', + type='ResNet'), + context_channels=( + 128, + 256, + 512, + ), + in_channels=3, + init_cfg=None, + norm_cfg=dict(requires_grad=True, type='SyncBN'), + out_channels=256, + out_indices=( + 0, + 1, + 2, + ), + spatial_channels=( + 64, + 64, + 64, + 128, + ), + type='BiSeNetV1'), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=256, + concat_input=False, + dropout_ratio=0.1, + in_channels=256, + in_index=0, + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=32, + num_convs=1, + type='FCNHead'), + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + clip_grad=None, + optimizer=dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005), + type='OptimWrapper') +optimizer = dict(lr=0.005, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict(begin=0, by_epoch=False, end=1500, start_factor=0.1, type='LinearLR'), + dict( + begin=1500, + by_epoch=False, + end=10000, + eta_min=0.0001, + power=0.9, + type='PolyLR'), +] +resume = False +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/3', seg_map_path='ann_dir/3'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict(type='CustomIoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict(max_iters=10000, type='IterBasedTrainLoop', val_interval=1000) +train_dataloader = dict( + batch_size=2, + dataset=dict( + data_prefix=dict( + img_path='img_dir/train', seg_map_path='ann_dir/train'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict(img_path='img_dir/eval', seg_map_path='ann_dir/eval'), + data_root='/media/ids/Ubuntu files/data/ARID20_CAT/', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ARID20CATDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +val_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + save_dir='show', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/bisenetv1_r18-d32-in1k-pre_1xb2-pre-coco-stuff164k-10k_arid20_cat-512x512' diff --git a/show/vis_data/vis_image/test_floor_bottom_seq03_2.png_1.png b/show/vis_data/vis_image/test_floor_bottom_seq03_2.png_1.png new file mode 100644 index 0000000000..a8807dad23 Binary files /dev/null and b/show/vis_data/vis_image/test_floor_bottom_seq03_2.png_1.png differ diff --git a/show/vis_data/vis_image/test_table_bottom_seq01_2.png_2.png b/show/vis_data/vis_image/test_table_bottom_seq01_2.png_2.png new file mode 100644 index 0000000000..85e7ad3ef2 Binary files /dev/null and b/show/vis_data/vis_image/test_table_bottom_seq01_2.png_2.png differ diff --git a/show/vis_data/vis_image/test_table_top_seq02_2.png_3.png b/show/vis_data/vis_image/test_table_top_seq02_2.png_3.png new file mode 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a/show/vis_data/vis_image/test_table_top_seq11_2.png_7.png b/show/vis_data/vis_image/test_table_top_seq11_2.png_7.png new file mode 100644 index 0000000000..4763a60056 Binary files /dev/null and b/show/vis_data/vis_image/test_table_top_seq11_2.png_7.png differ diff --git a/tests/custom/test_cfg.py b/tests/custom/test_cfg.py new file mode 100644 index 0000000000..10f0cf39f6 --- /dev/null +++ b/tests/custom/test_cfg.py @@ -0,0 +1,46 @@ +import argparse +import logging +import os +import os.path as osp + +from mmengine.config import Config, DictAction +from mmengine.logging import print_log +from mmengine.runner import Runner + +from mmseg.registry import RUNNERS +import torch + +def run_cfg(cfg, cfg_path): + torch.cuda.empty_cache() + # load config + + cfg.launcher = "none" + + # work_dir is determined in this priority: CLI > segment in file > filename + + cfg.work_dir = osp.join('./work_dirs', + osp.splitext(osp.basename(cfg_path))[0] + "_test") + + # maybe implement mixed precision training + + # build the runner from config + if 'runner_type' not in cfg: + # build the default runner + print("default runner") + # runner = Runner.from_cfg(cfg) + else: + # build customized runner from the registry + # if 'runner_type' is set in the cfg + print("runner type set") + # runner = RUNNERS.build(cfg) + + # start training + # runner.train() + +def main(): + cfg_path = "./configs/ddrnet/ddrnet_23-slim_in1k-pre_2xb6-10k_HOTS_v1-640x480.py" + cfg = Config.fromfile(cfg_path) + print(cfg.filename) + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/tools/analysis_tools/analyze_confusion_matrix_data.py b/tools/analysis_tools/analyze_confusion_matrix_data.py new file mode 100644 index 0000000000..68b678ee7c --- /dev/null +++ b/tools/analysis_tools/analyze_confusion_matrix_data.py @@ -0,0 +1,65 @@ +import json +import os +import argparse +from mmengine.fileio import dump + +def parse_args(): + parser = argparse.ArgumentParser(description='Analyze Json data') + parser.add_argument( + 'json_path', + type=str, + help='path of train log in json format') + parser.add_argument( + '--top_n', + type=int, + default=10 + ) + parser.add_argument( + '--ignore_background', + action='store_true' + ) + parser.add_argument('--save_dir_path', type=str, default=None) + args = parser.parse_args() + return args + + + +def get_top_n(dict_list, n = 10, metric ='score', reverse=True): + # sort_list = sorted(dict_list, key=itemgetter(metric), reverse=True) + sort_list = sorted(dict_list, key=lambda d : d[metric], reverse=reverse) + if len(sort_list) >= n: + return sort_list[:n] + return sort_list + + +def main(): + args = parse_args() + json_path = args.json_path + assert json_path.endswith('.json') + with open(json_path) as json_file: + json_data = json.load(json_file) + + if args.ignore_background: + json_data = [ + data_dict for data_dict in json_data + if "_background_" not in data_dict.values() + ] + top_n = get_top_n( + dict_list=json_data, + n=args.top_n, + metric='score' + ) + print(f"top {args.top_n} confusion values: ") + for conf_dict in top_n: + for key, val in conf_dict.items(): + print(f"{key} : {val}") + print() + + if args.save_dir_path is not None: + file_path = os.path.join( + args.save_dir_path, + f"top_{args.top_n}_confusion.json" + ) + dump(top_n, file_path, indent=4) +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/tools/analysis_tools/analyze_logs_.py b/tools/analysis_tools/analyze_logs_.py new file mode 100644 index 0000000000..a93edda076 --- /dev/null +++ b/tools/analysis_tools/analyze_logs_.py @@ -0,0 +1,165 @@ +# Copyright (c) OpenMMLab. All rights reserved. +"""Modified from https://github.com/open- +mmlab/mmdetection/blob/master/tools/analysis_tools/analyze_logs.py.""" +import argparse +import json +from collections import defaultdict + +import matplotlib.pyplot as plt +from matplotlib import rcParams, rcParamsDefault +import seaborn as sns +import numpy as np + +def reduce_zeros(iteration): + if iteration >= 1000: + return str( + int( + iteration / 1000 + ) + ) + "k" + return iteration + +def plot_curve(log_dicts, args): + if args.backend is not None: + plt.switch_backend(args.backend) + + # if legend is None, use {filename}_{key} as legend + legend = args.legend + if legend is None: + legend = [] + for json_log in args.json_logs: + for metric in args.keys: + legend.append(f'{json_log}_{metric}') + assert len(legend) == (len(args.json_logs) * len(args.keys)) + metrics = args.keys + + num_metrics = len(metrics) + for i, log_dict in enumerate(log_dicts): + epochs = list(log_dict.keys()) + for j, metric in enumerate(metrics): + print(f'plot curve of {args.json_logs[i]}, metric is {metric}') + plot_epochs = [] + plot_iters = [] + plot_values = [] + # In some log files exist lines of validation, + # `mode` list is used to only collect iter number + # of training line. + for epoch in epochs: + epoch_logs = log_dict[epoch] + if metric not in epoch_logs.keys(): + continue + if metric in ['mIoU', 'mAcc', 'aAcc']: + plot_epochs.append(epoch) + plot_values.append(epoch_logs[metric][0]) + else: + for idx in range(len(epoch_logs[metric])): + plot_iters.append(epoch_logs['step'][idx]) + plot_values.append(epoch_logs[metric][idx]) + ax = plt.gca() + label = legend[i * num_metrics + j] + if metric in ['mIoU', 'mAcc', 'aAcc']: + + plt.xlabel('step') + plt.plot(plot_epochs, plot_values, label=label, linewidth=6) + if args.reduce_zeros: + plot_epochs_reduced = [ + reduce_zeros(epoch) for epoch in plot_epochs + ] + ax.set_xticks(plot_epochs, plot_epochs_reduced) + else: + plt.xlabel('iter') + plt.plot(plot_iters, plot_values, label=label, linewidth=0.5) + plt.legend(loc='lower right') + if args.title is not None: + plt.title(args.title) + if args.out is None: + plt.show() + else: + print(f'save curve to: {args.out}') + plt.yticks(np.arange(0, 110, 10)) + plt.ylabel("mIoU") + plt.ylim(-1, 101) + plt.tight_layout() + plt.savefig(args.out) + plt.cla() + + +def parse_args(): + parser = argparse.ArgumentParser(description='Analyze Json Log') + parser.add_argument( + 'json_logs', + type=str, + nargs='+', + help='path of train log in json format') + parser.add_argument( + '--keys', + type=str, + nargs='+', + default=['mIoU'], + help='the metric that you want to plot') + parser.add_argument('--title', type=str, help='title of figure') + parser.add_argument( + '--legend', + type=str, + nargs='+', + default=None, + help='legend of each plot') + parser.add_argument( + '--backend', type=str, default=None, help='backend of plt') + parser.add_argument( + '--style', type=str, default='dark', help='style of plt') + parser.add_argument('--out', type=str, default=None) + parser.add_argument( + '--reduce_zeros', + '-rz', + action='store_true' + ) + args = parser.parse_args() + return args + + +def load_json_logs(json_logs): + # load and convert json_logs to log_dict, key is step, value is a sub dict + # keys of sub dict is different metrics + # value of sub dict is a list of corresponding values of all iterations + log_dicts = [dict() for _ in json_logs] + prev_step = 0 + for json_log, log_dict in zip(json_logs, log_dicts): + with open(json_log) as log_file: + for line in log_file: + log = json.loads(line.strip()) + # the final step in json file is 0. + if 'step' in log and log['step'] != 0: + step = log['step'] + prev_step = step + else: + step = prev_step + if step not in log_dict: + log_dict[step] = defaultdict(list) + for k, v in log.items(): + log_dict[step][k].append(v) + return log_dicts + + +def main(): + rcParams.update( + { + 'figure.figsize' : (12, 10), + 'legend.fontsize' : 30, + 'axes.labelsize' : 30, + 'axes.titlesize' : 30, + 'xtick.labelsize' : 30, + 'ytick.labelsize' : 30, + 'lines.linewidth' : 6 + } + ) + args = parse_args() + json_logs = args.json_logs + for json_log in json_logs: + assert json_log.endswith('.json') + log_dicts = load_json_logs(json_logs) + plot_curve(log_dicts, args) + rcParams.update(rcParamsDefault) + +if __name__ == '__main__': + main() diff --git a/tools/analysis_tools/benchmark.py b/tools/analysis_tools/benchmark.py index afaeabac85..73b851de39 100644 --- a/tools/analysis_tools/benchmark.py +++ b/tools/analysis_tools/benchmark.py @@ -13,7 +13,7 @@ from mmengine.utils import mkdir_or_exist from mmseg.registry import MODELS - +from mmengine.device import get_max_cuda_memory def parse_args(): parser = argparse.ArgumentParser(description='MMSeg benchmark a model') @@ -54,11 +54,14 @@ def main(): benchmark_dict = dict(config=args.config, unit='img / s') overall_fps_list = [] + overall_mem_list = [] cfg.test_dataloader.batch_size = 1 for time_index in range(repeat_times): print(f'Run {time_index + 1}:') # build the dataloader - data_loader = Runner.build_dataloader(cfg.test_dataloader) + cfg_test_dataloader = cfg.test_dataloader + cfg_test_dataloader.sampler = dict(shuffle=True, type='InfiniteSampler') + data_loader = Runner.build_dataloader(cfg_test_dataloader) # build the model and load checkpoint cfg.model.train_cfg = None @@ -75,7 +78,7 @@ def main(): model.eval() # the first several iterations may be very slow so skip them - num_warmup = 5 + num_warmup = 10 pure_inf_time = 0 total_iters = 200 @@ -107,13 +110,20 @@ def main(): print(f'Overall fps: {fps:.2f} img / s\n') benchmark_dict[f'overall_fps_{time_index + 1}'] = round(fps, 2) overall_fps_list.append(fps) + + mem = get_max_cuda_memory(torch.cuda.current_device()) + benchmark_dict[f'overall_memory_{time_index + 1}'] = mem + overall_mem_list.append(mem) break - benchmark_dict['average_fps'] = round(np.mean(overall_fps_list), 2) - benchmark_dict['fps_variance'] = round(np.var(overall_fps_list), 4) + + benchmark_dict['FPS'] = round(np.mean(overall_fps_list), 2) + benchmark_dict['Var FPS'] = round(np.var(overall_fps_list), 4) + benchmark_dict['Mem (MB)'] = round(np.mean(overall_mem_list), 2) + benchmark_dict['Var Mem'] = round(np.var(overall_mem_list), 4) print(f'Average fps of {repeat_times} evaluations: ' - f'{benchmark_dict["average_fps"]}') + f'{benchmark_dict["FPS"]}') print(f'The variance of {repeat_times} evaluations: ' - f'{benchmark_dict["fps_variance"]}') + f'{benchmark_dict["Var FPS"]}') dump(benchmark_dict, json_file, indent=4) diff --git a/tools/analysis_tools/confusion_matrix.py b/tools/analysis_tools/confusion_matrix.py index 39756cdfdd..f5a2c9163d 100644 --- a/tools/analysis_tools/confusion_matrix.py +++ b/tools/analysis_tools/confusion_matrix.py @@ -8,6 +8,7 @@ from mmengine.config import Config, DictAction from mmengine.registry import init_default_scope from mmengine.utils import mkdir_or_exist, progressbar +from mmengine.fileio import dump from PIL import Image from mmseg.registry import DATASETS @@ -64,15 +65,23 @@ def calculate_confusion_matrix(dataset, results): res_segm = per_img_res gt_segm = dataset[idx]['data_samples'] \ .gt_sem_seg.data.squeeze().numpy().astype(np.uint8) + gt_segm, res_segm = gt_segm.flatten(), res_segm.flatten() if reduce_zero_label: gt_segm = gt_segm - 1 to_ignore = gt_segm == ignore_index - + gt_segm, res_segm = gt_segm[~to_ignore], res_segm[~to_ignore] - inds = n * gt_segm + res_segm - mat = np.bincount(inds, minlength=n**2).reshape(n, n) - confusion_matrix += mat + + # inds = n * gt_segm + res_segm + # print(np.unique(inds)) + # print(max(inds)) + # mat = np.bincount(inds, minlength=n**2).reshape(n, n) + # print(np.unique(mat)) + + # confusion_matrix += mat + for gt_pix, pred_pix in zip(gt_segm, res_segm): + confusion_matrix[gt_pix][pred_pix] += 1 prog_bar.update() return confusion_matrix @@ -98,10 +107,11 @@ def plot_confusion_matrix(confusion_matrix, per_label_sums = confusion_matrix.sum(axis=1)[:, np.newaxis] confusion_matrix = \ confusion_matrix.astype(np.float32) / per_label_sums * 100 - + confusion_matrix = np.nan_to_num(confusion_matrix) + num_classes = len(labels) fig, ax = plt.subplots( - figsize=(2 * num_classes, 2 * num_classes * 0.8), dpi=300) + figsize=(min(2 * num_classes, 100), min(2 * num_classes * 0.8, 80)), dpi=300) cmap = plt.get_cmap(color_theme) im = ax.imshow(confusion_matrix, cmap=cmap) colorbar = plt.colorbar(mappable=im, ax=ax) @@ -131,7 +141,6 @@ def plot_confusion_matrix(confusion_matrix, ax.set_yticks(np.arange(num_classes)) ax.set_xticklabels(labels, fontsize=20) ax.set_yticklabels(labels, fontsize=20) - ax.tick_params( axis='x', bottom=False, top=True, labelbottom=False, labeltop=True) plt.setp( @@ -150,18 +159,49 @@ def plot_confusion_matrix(confusion_matrix, va='center', color='k', size=20) - ax.set_ylim(len(confusion_matrix) - 0.5, -0.5) # matplotlib>3.1.1 - fig.tight_layout() + # if show: + # plt.show() if save_dir is not None: mkdir_or_exist(save_dir) plt.savefig( - os.path.join(save_dir, 'confusion_matrix.png'), format='png') + os.path.join(save_dir, 'confusion_matrix.png'), bbox_inches='tight') + if show: plt.show() - +def dump_confusion_data( + confusion_matrix, labels, + min_value = 0.0, save_dir=None, + ignore_diagonal = True +): + per_label_sums = confusion_matrix.sum(axis=1)[:, np.newaxis] + confusion_matrix = \ + confusion_matrix.astype(np.float32) / per_label_sums * 100 + confusion_matrix = np.nan_to_num(confusion_matrix) + conf_dict_list = [] + # row nr + for gt_label_idx, gt_label in enumerate(labels): + + for pred_label_idx, pred_label in enumerate(labels): + if ignore_diagonal and gt_label_idx == pred_label_idx: + continue + conf_value = confusion_matrix[gt_label_idx][pred_label_idx] + if conf_value < min_value: + continue + conf_dict_list.append( + { + "gt_label" : gt_label, + "pred_label" : pred_label, + "score" : conf_value + } + ) + if save_dir is not None: + mkdir_or_exist(save_dir) + json_file = os.path.join(save_dir, "confusion.json") + dump(conf_dict_list, json_file, indent=4) + def main(): args = parse_args() @@ -184,6 +224,11 @@ def main(): dataset = DATASETS.build(cfg.test_dataloader.dataset) confusion_matrix = calculate_confusion_matrix(dataset, results) + dump_confusion_data( + confusion_matrix=confusion_matrix, + labels=dataset.METAINFO['classes'], + save_dir=args.save_dir + ) plot_confusion_matrix( confusion_matrix, dataset.METAINFO['classes'], diff --git a/tools/analysis_tools/get_flops.py b/tools/analysis_tools/get_flops.py index 78a73988d4..587c3a6f45 100644 --- a/tools/analysis_tools/get_flops.py +++ b/tools/analysis_tools/get_flops.py @@ -8,11 +8,13 @@ from mmengine.logging import MMLogger from mmengine.model import revert_sync_batchnorm from mmengine.registry import init_default_scope - +from mmengine.fileio import dump from mmseg.models import BaseSegmentor from mmseg.registry import MODELS from mmseg.structures import SegDataSample - +import os.path as osp +from mmengine.utils import mkdir_or_exist +import time try: from mmengine.analysis import get_model_complexity_info from mmengine.analysis.print_helper import _format_size @@ -30,6 +32,10 @@ def parse_args(): nargs='+', default=[2048, 1024], help='input image size') + parser.add_argument( + '--work-dir', + help=('if specified, the results will be dumped ' + 'into the directory as json')) parser.add_argument( '--cfg-options', nargs='+', @@ -100,7 +106,16 @@ def main(): args = parse_args() logger = MMLogger.get_instance(name='MMLogger') - + timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) + if args.work_dir is not None: + mkdir_or_exist(osp.abspath(args.work_dir)) + json_file = osp.join(args.work_dir, f'flops_{timestamp}.json') + else: + # use config filename as default work_dir if cfg.work_dir is None + work_dir = osp.join('./work_dirs', + osp.splitext(osp.basename(args.config))[0]) + mkdir_or_exist(osp.abspath(work_dir)) + json_file = osp.join(work_dir, f'flops_{timestamp}.json') result = inference(args, logger) split_line = '=' * 30 ori_shape = result['ori_shape'] @@ -118,6 +133,7 @@ def main(): print('!!!Please be cautious if you use the results in papers. ' 'You may need to check if all ops are supported and verify ' 'that the flops computation is correct.') + dump(result, json_file, indent=4) if __name__ == '__main__': diff --git a/tools/clutter_test.py b/tools/clutter_test.py new file mode 100644 index 0000000000..9c697c1e88 --- /dev/null +++ b/tools/clutter_test.py @@ -0,0 +1,77 @@ + +import argparse +import os +import os.path as osp + +from mmengine.config import Config, DictAction +from mmengine.runner import Runner + + +# ONLY WORKS FOR ARID20_CAT + +# load cfg +# change test_path testloader to n in [0-19] +# run test for each n +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument('config', help='train config file path') + parser.add_argument('checkpoint', help='checkpoint file') + parser.add_argument( + '--work-dir', + help=('if specified, the evaluation metric results will be dumped' + 'into the directory as json')) + args = parser.parse_args() + + + return args + + + +def main(): + args = parse_args() + + # load config + cfg = Config.fromfile(args.config) + + if args.work_dir is not None: + # update configs according to CLI args if args.work_dir is not None + cfg.work_dir = args.work_dir + elif cfg.get('work_dir', None) is None: + # use config filename as default work_dir if cfg.work_dir is None + cfg.work_dir = osp.join('./work_dirs', + osp.splitext(osp.basename(args.config))[0]) + + cfg.load_from = args.checkpoint + + work_dir = os.path.join(cfg.work_dir, "clutter") + + if not os.path.exists(work_dir): + os.makedirs(work_dir) + + test_dataloader = cfg.test_dataloader + + dataset_root = test_dataloader["dataset"]["data_root"] + clutter_dir_path = os.path.join(dataset_root, "img_dir", "clutter") + if not os.path.exists(clutter_dir_path): + print(f"path {clutter_dir_path} does not exists. clutter test failed") + return + + for n_objects_dir in os.listdir(clutter_dir_path): + + if not n_objects_dir.isnumeric(): + continue + test_dataloader["dataset"]["data_prefix"] = dict( + img_path=os.path.join("img_dir", "clutter", n_objects_dir), + seg_map_path=os.path.join("ann_dir", "clutter", n_objects_dir) + ) + cfg.test_dataloader = test_dataloader + cfg.work_dir = os.path.join(work_dir, n_objects_dir) + + runner = Runner.from_cfg(cfg) + + # start testing + runner.test() + + +if __name__ == '__main__': + main() diff --git a/tools/find_selection.py b/tools/find_selection.py new file mode 100644 index 0000000000..7759ad6699 --- /dev/null +++ b/tools/find_selection.py @@ -0,0 +1,812 @@ + +from train_set.dict_utils import TrimData +import os +from mmengine import Config +from copy import copy, deepcopy +import markdown +import re +from mmengine.analysis.complexity_analysis import ( + parameter_count_table, + parameter_count +) +from mmengine.registry import init_default_scope + +from mmseg.registry import MODELS + +def parse_row(row_str): + row_ = row_str.split("|") + row = [] + for item in row_: + if item == "" or item == "\n": + continue + row.append(item.strip()) + return row + +def is_table_row(line): + if len(line) < 2: + return False + return line[0] == "|" and line[-1] == "|" + +def is_first_row(row): + return row[0] == "Method" and row[1] == "Backbone" + +def is_sep_row(row): + return "--" in row[0] + +def extract_dataset_name(line): + if len(line) < 4: + return "" + if not line[:3] == "###": + return "" + trimmed_line = line[4:] + trimmed_line = trimmed_line[:-1] + return trimmed_line + +def same_model_diff_sched(model_dict0, model_dict1): + if model_dict0 is model_dict1: + return False + if model_dict0["Method"] != model_dict1["Method"]: + return False + if model_dict0["Backbone"] != model_dict1["Backbone"]: + return False + + if model_dict0["Crop Size"] != model_dict1["Crop Size"]: + return False + + if model_dict0["dataset"] != model_dict1["dataset"]: + if ( + ( + model_dict0["dataset"] == "Pascal Context" + and + model_dict1["dataset"] == "Pascal Context 59" + ) + or + ( + model_dict1["dataset"] == "Pascal Context" + and + model_dict0["dataset"] == "Pascal Context 59" + ) + ): + return True + return False + return True + +def complete_model_dict(model_dict, model_dict_list): + for model_dict_ in model_dict_list: + if model_dict is model_dict_: + continue + if same_model_diff_sched( + model_dict0=model_dict_, + model_dict1=model_dict + ): + if model_dict["Mem (GB)"] == '-' and model_dict_["Mem (GB)"] != '-': + model_dict["Mem (GB)"] = model_dict_["Mem (GB)"] + if ( + model_dict["Inf time (fps)"] == '-' + and + model_dict_["Inf time (fps)"] != '-' + ): + model_dict["Inf time (fps)"] = model_dict_["Inf time (fps)"] + return model_dict + return None # TODO not sure just for test + + +def fill_in_missing_vals( + model_dict_list, + keys_to_fix = [ + "Mem (GB)", + "Inf time (fps)", + "mIoU" + ] +): + model_dict_list_ = [] + for model_dict in model_dict_list: + if '-' in [ + value for key, value in model_dict.items() if key in keys_to_fix + ]: + # if '-' in model_dict.values(): + model_dict = complete_model_dict( + model_dict=model_dict, + model_dict_list=model_dict_list + ) + if model_dict is None: + continue + model_dict_list_.append(model_dict) + return model_dict_list_ + +def fix_dict_types( + model_dict_list, + keys_to_fix = [ + "Mem (GB)", + "Inf time (fps)", + "mIoU" + ] +): + model_dict_list_ = [] + for model_dict in model_dict_list: + for key in keys_to_fix: + try: + val = re.findall(r"[-+]?(?:\d*\.*\d+)", model_dict[key])[0] + + except: + continue + model_dict[key] = float(val) + model_dict_list_.append(model_dict) + return model_dict_list_ + +def dict_keys_valid( + model_dict, + essential_keys = [ + "Method", + "Backbone", + "Crop Size", + "dataset", + "Mem (GB)", + "Inf time (fps)", + "mIoU" + ] +): + for key in essential_keys: + if key not in model_dict.keys(): + + + return False + return True + +def comparing_dict_vals_valid( + model_dict, + comparing_keys = [ + "Mem (GB)", + "Inf time (fps)", + "mIoU" + ] +): + for key in comparing_keys: + if not isinstance(model_dict[key], (int, float)): + return False + return True +def get_comparable_model_dict_list( + model_dict_list, + comparing_keys = [ + "Mem (GB)", + "Inf time (fps)", + "mIoU" + ] +): + model_dict_list_ = [] + for model_dict in model_dict_list: + if not comparing_dict_vals_valid( + model_dict=model_dict, + comparing_keys=comparing_keys + ): + continue + model_dict_list_.append(model_dict) + + return model_dict_list_ + +def remove_invalid_dicts( + model_dict_list, + essential_keys = [ + "Method", + "Backbone", + "Crop Size", + "dataset", + "Mem (GB)", + "Inf time (fps)", + "mIoU" + ] +): + model_dict_list_ = [] + for model_dict in model_dict_list: + + if model_dict is None: + continue + if not dict_keys_valid( + model_dict=model_dict, essential_keys=essential_keys + ): + continue + + if not os.path.exists(model_dict["config"]): + continue + model_dict_list_.append(model_dict) + return model_dict_list_ + +def is_valid_dataset_name( + dataset_name, + accepted_dataset_list = copy(TrimData.accepted_dataset_list) +): + return dataset_name in accepted_dataset_list + +def is_dataset_name( + line, + accepted_dataset_list = copy(TrimData.accepted_dataset_list) +): + if len(line) < 4: + return False + if not line[:3] == "###": + return False + dataset_name = extract_dataset_name(line=line) + if dataset_name == "": + return False + return True + +def make_model_dict(row, data_set_name, labels): + model_dict = {} + for label in labels: + if label == "download": + continue + if label == "config": + config_path = row[labels.index(label)].split("main/")[-1][:-1] + model_dict["config"] = config_path + continue + model_dict[label] = row[labels.index(label)] + model_dict["dataset"] = data_set_name + return model_dict + +def get_readme_data( + project_path, + accepted_dataset_list = copy(TrimData.accepted_dataset_list) +): + readme_path = os.path.join(project_path, "README.md") + if not os.path.exists(readme_path): + return None + model_dict_list = [] + current_dataset_name = "" + labels = [] + + with open(readme_path, 'r') as readme_file: + for line in readme_file.readlines(): + if is_dataset_name(line=line): + current_dataset_name = extract_dataset_name(line=line) + + if not is_valid_dataset_name(current_dataset_name): + continue + + if is_table_row(line=line[:-1]): + row = parse_row(row_str=line) + if is_sep_row(row=row): + continue + + if is_first_row(row=row): + labels = row + else: + model_dict_list.append(make_model_dict( + row=row, + data_set_name=current_dataset_name, + labels=labels + ) + ) + return model_dict_list + +def postprocesses_model_dict_list(model_dict_list): + model_dict_list = remove_invalid_dicts(model_dict_list=model_dict_list) + if model_dict_list == []: + return [] + # fill in missing values + model_dict_list = fill_in_missing_vals(model_dict_list=model_dict_list) + + # fix types + model_dict_list = fix_dict_types(model_dict_list=model_dict_list) + + # filter out last faulty data + model_dict_list = get_comparable_model_dict_list( + model_dict_list=model_dict_list + ) + + return model_dict_list + +def get_all_model_dicts( + project_list, + config_root_path = "configs/", + accepted_dataset_list = copy(TrimData.accepted_dataset_list) +): + + model_dict_list = [] + for project_name in project_list: + project_path = os.path.join(config_root_path, project_name) + project_model_dict_list = get_readme_data( + project_path=project_path, + accepted_dataset_list=accepted_dataset_list + ) + + if project_model_dict_list is None: + continue + + project_model_dict_list = postprocesses_model_dict_list( + model_dict_list=project_model_dict_list + ) + model_dict_list += project_model_dict_list + return model_dict_list + + + +class ScoreCollection: + + calc_options = [ + "default", + "fps_per_GB", + "mIoU_per_spf" + ] + + @staticmethod + def get_score( + model_dict, + method = "default", + factors = { + "mem_factor" : 1, + "fps_factor" : 1, + "mIoU_factor" : 1 + } + ): + if method == "fps_per_GB": + return ScoreCollection.fps_per_GB( + model_dict=model_dict, + mem_factor=factors["mem_factor"], + fps_factor=factors["fps_factor"] + ) + if method == "mIoU_per_spf": + return ScoreCollection.mIoU_per_spf( + model_dict=model_dict + ) + return ScoreCollection.default_calc( + model_dict=model_dict, + mem_factor=factors["mem_factor"], + fps_factor=factors["fps_factor"], + mIoU_factor=factors["mIoU_factor"] + ) + @staticmethod + def default_calc( + model_dict, + mem_factor = 1, + fps_factor = 1, + mIoU_factor = 1 + ): + num = ( + fps_factor * model_dict["Inf time (fps)"] + + + mIoU_factor * model_dict["mIoU"] + ) + den = mem_factor * model_dict["Mem (GB)"] + if den == 0: + den = 1 + return num / den + @staticmethod + def fps_per_GB( + model_dict, + mem_factor = 1, + fps_factor = 1 + ): + num = fps_factor * model_dict["Inf time (fps)"] + den = mem_factor * model_dict["Mem (GB)"] + if den == 0: + den = 1 + return num / den + + # inverse of fps + @staticmethod + def mIoU_per_spf( + model_dict + ): + num = model_dict["mIoU"] + den = 1.0 / model_dict["Inf time (fps)"] + if den == 0: + den = 1 + return num / den + +def add_custom_score( + model_dict_list, + score_name = "custom_score", + calc_method = "default", + factors = { + "mem_factor" : 1, + "fps_factor" : 1, + "mIoU_factor" : 1 + } +): + + + for model_dict in model_dict_list: + model_dict[score_name] = ScoreCollection.get_score( + model_dict=model_dict, + method=calc_method, + factors=factors + ) + return model_dict_list + +def add_n_params_to_model_dicts(model_dict_list): + for model_dict in model_dict_list: + cfg = Config.fromfile(model_dict["config"]) + init_default_scope(cfg.get('default_scope', 'mmseg')) + cfg.model.train_cfg = None + model = MODELS.build(cfg.model) + model_dict["n_params"] = parameter_count(model=model)[''] + return model_dict_list + +def sorted_by_metric( + model_dict_list, + metric ='mIoU', + descending=True +): + sort_list = sorted( + model_dict_list, key=lambda d : d[metric], reverse=descending + ) + return sort_list + + +def print_trimmed_model_dict( + model_dict +): + config_path = model_dict["config"] + print(f"config : {config_path}") + for key, val in model_dict.items(): + if isinstance(val, (int, float)): + print(f"{key} : {val}") + +config_root_path = "configs/" + + +project_list = [ + project for project in os.listdir(config_root_path) + if not project == "_base_" +] +for project_name in project_list: + print(project_name) +model_dict_list = get_all_model_dicts( + project_list=project_list +) + +print(len(model_dict_list)) + + + +model_dict_list = add_custom_score( + model_dict_list=model_dict_list, + score_name="custom_score" +) + +model_dict_list = add_custom_score( + model_dict_list=model_dict_list, + score_name="fps_per_GB", + calc_method="fps_per_GB" +) + +model_dict_list = add_custom_score( + model_dict_list=model_dict_list, + score_name="mIoU_per_spf", + calc_method="mIoU_per_spf" +) + +# One very specific case: +for model_dict in model_dict_list: + if "." not in str(model_dict["Mem (GB)"])[:3]: + model_dict["Mem (GB)"] = model_dict["Mem (GB)"] / 1000.0 + + +def get_batch_size_from_cfg_name(cfg_name): + return int( + cfg_name.split("xb")[-1].split("-")[0] + ) + + + +# filter on memory +# TODO: not very good estimator +def remove_too_big_models(model_dict_list, max_mem_per_batch = 2.5): + model_dict_list_ = [] + for model_dict in model_dict_list: + cfg_name = model_dict["config"].split("/")[-1] + batch_size = get_batch_size_from_cfg_name( + cfg_name=cfg_name + ) + if model_dict["Mem (GB)"] / float(batch_size) > max_mem_per_batch: + continue + model_dict_list_.append( + model_dict + ) + return model_dict_list_ + +def get_best_model(model_group, metric = "mIoU"): + return sorted_by_metric( + model_dict_list=model_group, + metric=metric, + descending=True + )[0] + + +def get_unique_model_list(model_dict_list): + unique_list = [] + for model_dict in model_dict_list: + if len( + [ + m_dict for m_dict in unique_list + if same_model_diff_sched( + model_dict0=model_dict, + model_dict1=m_dict + ) or model_dict["config"] == m_dict["config"] + ] + ) > 0: + continue + model_group = [model_dict] + for model_dict1 in model_dict_list: + if model_dict is model_dict1: + continue + if same_model_diff_sched( + model_dict0=model_dict, + model_dict1=model_dict1 + ): + model_group.append(model_dict1) + unique_list.append( + get_best_model(model_group=model_group) + ) + return unique_list + + +# model_dict_list = add_n_params_to_model_dicts(model_dict_list=model_dict_list) + +def record_score(model_dict, score_list, rank, n_items, metric): + + rank_score = n_items - rank + if metric == "Inf time (fps)" or metric == "mIoU": + rank_score *= 2 + if model_dict["config"] not in [ + item["config"] for item in score_list + ]: + model_dict = deepcopy(model_dict) + model_dict["score"] = { + "count" : 1, + "metric_results" : [ + { + "rank" : rank, + "metric" : metric + } + ], + "total_rank_score" : rank_score + } + score_list.append( + model_dict + ) + return score_list + for item in score_list: + if item["config"] == model_dict["config"]: + item["score"]["count"] += 1 + item["score"]["metric_results"].append( + { + "rank" : rank, + "metric" : metric + } + ) + item["score"]["total_rank_score"] += rank_score + break + return score_list + + + +def get_sorted_by_metric_for_dataset( + dataset_name, + model_dict_list, + metric_param_list +): + + model_dict_list_ = [ + model_dict for model_dict in model_dict_list + if model_dict["dataset"] == dataset_name + ] + sorted_by_metric_dict = {} + for param in metric_param_list: + + sorted_dict_list = sorted_by_metric( + model_dict_list=model_dict_list_, + metric=param["metric"], + descending=param["descending"] + ) + sorted_by_metric_dict[param["metric"]] = sorted_dict_list + return sorted_by_metric_dict + +def get_top_n_metric_dict( + sorted_by_metric_dict, + n_items = 10 +): + top_n_metric_dict = {} + for metric, sorted_dict_list in sorted_by_metric_dict.items(): + top_n_metric_dict[metric] = sorted_dict_list[:n_items] + return top_n_metric_dict + +def print_n_top_items(top_n_metric_dict): + for metric, top_n in top_n_metric_dict.items(): + print(f"\ntop {len(top_n)} of {metric}:\n") + rank = 1 + for model_dict in top_n: + print(f"Rank: {rank}") + print_trimmed_model_dict(model_dict=model_dict) + print() + rank += 1 + +def get_score_list_dataset(top_n_metric_dict): + score_list = [] + for metric, top_n in top_n_metric_dict.items(): + rank = 1 + for model_dict in top_n: + score_list = record_score( + model_dict=model_dict, + score_list=score_list, + rank=rank, + n_items=n_items, + metric=metric + ) + rank += 1 + return score_list + + + +def sorted_score(score_list, n_items = 10): + sorted_score_list = sorted( + score_list, + key=lambda d : d["score"]["total_rank_score"], + reverse=True + ) + if len(sorted_score_list) >= n_items: + return sorted_score_list[:n_items] + return sorted_score_list + + +def print_sorted_score(sorted_score_list): + print(f"sorted scores ({len(sorted_score_list)}): ") + for moded_model_dict in sorted_score_list: + print_trimmed_model_dict(model_dict=moded_model_dict) + print("scores: ") + for key, val in moded_model_dict["score"].items(): + if key == "metric_results": + sorted_rank = sorted( + val, + key=lambda item: item["rank"] + ) + print(f"{key}:") + for record in sorted_rank: + print(f" - rank : {record['rank']}, metric : {record['metric']}") + continue + print(f"{key} : {val}") + print() + + + +def write_selected_files(selected_model_dict_list): + for model_dict in selected_model_dict_list: + + cfg_path = model_dict["config"] + cfg_dir = cfg_path.split("/")[-2] + lines = [] + with open(cfg_path, 'r') as cfg_file: + lines = cfg_file.readlines() + cfg_name = cfg_path.split("/")[-1] + dst_path = os.path.join( + "configs/my_configs/", + cfg_name + ) + with open(dst_path, 'w') as dest_file: + for line in lines: + if line[:len("_base_")] == "_base_": + final_part_line = line.split('=')[-1] + final_part_line = final_part_line.strip() + # multiple base files + if final_part_line == '[': + pass + if final_part_line[:len("'./")] == "'./": + base_name = final_part_line[len("'./"):-1] + base_path = os.path.join("../", cfg_dir, base_name) + dest_file.write(f"_base_ = '{base_path}'\n") + continue + if final_part_line[:len("['./")] == "['./": + base_name = final_part_line[len("['./"):-2] + base_path = os.path.join("../", cfg_dir, base_name) + dest_file.write(f"_base_ = ['{base_path}']\n") + continue + dest_file.write(f"{line}") + +def select_configs(selected_model_dict_list, provisonal_list): + for provisional_model_dict in provisonal_list: + if provisional_model_dict["config"] in [ + model_dict["config"] for model_dict in selected_model_dict_list + ]: + continue + selected_model_dict_list.append( + provisional_model_dict + ) + return selected_model_dict_list + + +metric_param_list = [ + { + "metric" : "custom_score", + "descending" : True + }, + { + "metric" : "fps_per_GB", + "descending" : True + }, + { + "metric" : "mIoU_per_spf", + "descending" : True + }, + { + "metric" : "Inf time (fps)", + "descending" : True + }, + { + "metric" : "mIoU", + "descending" : True + } +] + +# model_dict_list = add_n_params_to_model_dicts(model_dict_list=model_dict_list) + +model_dict_list = remove_too_big_models(model_dict_list=model_dict_list) + +# print(f"len model dict list (pre unique): {len(model_dict_list)}") +# for model_dict in model_dict_list: +# print_trimmed_model_dict(model_dict=model_dict) + +model_dict_list = get_unique_model_list(model_dict_list=model_dict_list) + +# print(f"len model dict list (unique): {len(model_dict_list)}") +# for model_dict in model_dict_list: +# print_trimmed_model_dict(model_dict=model_dict) + + +selected_model_dict_list = [] +n_items = 5 # TODO maybe lower, look at result +n_items_provisional = 20 +n_total_models = 0 +for dataset_name in TrimData.accepted_dataset_list: + print(f"Dataset: {dataset_name}") + # Use a bigger provisional selection in order for more generalistic + # model to be added + sorted_by_metric_dict = get_sorted_by_metric_for_dataset( + dataset_name=dataset_name, + model_dict_list=model_dict_list, + metric_param_list=metric_param_list + ) + top_n_metric_dict = get_top_n_metric_dict( + sorted_by_metric_dict=sorted_by_metric_dict, + n_items=n_items_provisional + ) + score_list = get_score_list_dataset(top_n_metric_dict=top_n_metric_dict) + # finer selection + for metric, top_n in top_n_metric_dict.items(): + top_n_metric_dict[metric] = top_n[:n_items] + + print_n_top_items(top_n_metric_dict=top_n_metric_dict) + + sorted_score_list = sorted_score(score_list=score_list, n_items=n_items) + print_sorted_score(sorted_score_list=sorted_score_list) + + + + for metric, top_n in top_n_metric_dict.items(): + selected_model_dict_list = select_configs( + selected_model_dict_list=selected_model_dict_list, + provisonal_list=top_n + ) + n_total_models += len(top_n) + + selected_model_dict_list = select_configs( + selected_model_dict_list=selected_model_dict_list, + provisonal_list=sorted_score_list + ) + n_total_models += len(sorted_score_list) + + + print(f"n_total_models : {n_total_models}") + print(f"len selection : {len(selected_model_dict_list)}") + + + print('#' * 80) + +# for model_dict in sel + +write_selected_files(selected_model_dict_list=selected_model_dict_list) + +print("SELECTED MODELS:") +for model_dict in selected_model_dict_list: + for key, val in model_dict.items(): + print(f"{key} : {val}") + print() \ No newline at end of file diff --git a/tools/misc/gen_configs_hots.py b/tools/misc/gen_configs_hots.py new file mode 100644 index 0000000000..9c0d13e98b --- /dev/null +++ b/tools/misc/gen_configs_hots.py @@ -0,0 +1,47 @@ +import os + +def gen_iterations(cfg_dir_path, cfg_name, iters = ["20k", "40k", "80k", "160k"]): + existing_iter_file = "20k" + for iter in iters: + if iter in cfg_name: + existing_iter_file = iter + for iter in iters: + if iter == existing_iter_file: + continue + new_cfg = cfg_name.replace(existing_iter_file, iter) + source_path = os.path.join(cfg_dir_path, cfg_name) + target_path = os.path.join(cfg_dir_path, new_cfg) + os.system(f"cp {source_path} {target_path}") + + + + +def get_all_iters_of_cfg(cfg_name, iters = ["20k", "40k", "80k", "160k"]): + names = [cfg_name] + for iter in iters: + if iter in cfg_name: + existing_iter_file = iter + for iter in iters: + if iter != existing_iter_file: + names.append(cfg_name.replace(existing_iter_file, iter)) + return names + + +def gen_pretrained(cfg_dir_path, cfg_name, pretrained_appendix): + cfg_names = get_all_iters_of_cfg(cfg_name=cfg_name) + for cfg_name in cfg_names: + source_path = os.path.join(cfg_dir_path, cfg_name) + target_path = os.path.join(cfg_dir_path, + cfg_name.replace(".py", f"{pretrained_appendix}.py")) + os.system(f"cp {source_path} {target_path}") + +cfg_dir_path = "configs/fastscnn/" +cfg_name = "pspnet_r18-d8_4xb2-10k_HOTS_v1-640x480.py" + +gen_iterations(cfg_dir_path=cfg_dir_path, cfg_name=cfg_name, iters=["20k"]) + +# gen_pretrained( +# cfg_dir_path = cfg_dir_path, +# cfg_name = cfg_name, +# pretrained_appendix="_pretrained_ade20k" +# ) \ No newline at end of file diff --git a/tools/test_selection.py b/tools/test_selection.py new file mode 100644 index 0000000000..ce665b7a80 --- /dev/null +++ b/tools/test_selection.py @@ -0,0 +1,296 @@ +import os +import subprocess +import argparse + + +def run_performance_test( + cfg_path, checkpoint_path, + work_dir_path, prediction_result_path, + show_dir_path +): + call_list = ["python", "tools/test.py"] + call_list.append(cfg_path) + call_list.append(checkpoint_path) + + # specify workdir arg + call_list.append("--work-dir") + call_list.append(work_dir_path) + + # # out args + call_list.append("--out") + call_list.append(prediction_result_path) + + # # activate show + # call_list.append("--show") + # Add show dir arg + call_list.append("--show-dir") + call_list.append(show_dir_path) + + subprocess.call(call_list) + +def run_benchmark( + cfg_path, checkpoint_path, + work_dir_path, repeat_times = 1 +): + call_list = ["python", "tools/analysis_tools/benchmark.py"] + call_list.append(cfg_path) + call_list.append(checkpoint_path) + + # specify workdir arg + call_list.append("--work-dir") + call_list.append(work_dir_path) + + call_list.append("--repeat-times") + call_list.append(str(repeat_times)) + + subprocess.call(call_list) + + +def run_clutter_test( + cfg_path, checkpoint_path, + work_dir_path +): + # run clutter experiment + call_list = ["python", "tools/clutter_test.py"] + call_list.append(cfg_path) + call_list.append(checkpoint_path) + + # specify workdir arg + call_list.append("--work-dir") + call_list.append(work_dir_path) + subprocess.call(call_list) + +# def run_scene_test( +# cfg_path, checkpoint_path, +# work_dir_path +# ): +# # run scene experiment +# call_list = ["python", "my_projects/ablation_tests/scripts/scene_test.py"] +# call_list.append(cfg_path) +# call_list.append(checkpoint_path) + +# # specify workdir arg +# call_list.append("--work-dir") +# call_list.append(work_dir_path) +# subprocess.call(call_list) + +def run_confusion_matrix( + cfg_path, prediction_result_path, + confusion_matrix_save_path +): + call_list = ["python", "tools/analysis_tools/confusion_matrix.py"] + + call_list.append(cfg_path) + + call_list.append(prediction_result_path) + + call_list.append(confusion_matrix_save_path) + + subprocess.call(call_list) + +def run_confusion_analysis( + json_path, save_dir_path, top_n = 10 +): + call_list = ["python"] + call_list.append("tools/analysis_tools/analyze_confusion_matrix_data.py") + + call_list.append(json_path) + + call_list.append("--top_n") + call_list.append(str(top_n)) + + call_list.append("--ignore_background") + + call_list.append("--save_dir_path") + call_list.append(save_dir_path) + + subprocess.call(call_list) + + +def run_flops( + cfg_path, work_dir_path, shape = [3, 512, 512] +): + call_list = ["python", "tools/analysis_tools/get_flops.py"] + + call_list.append(cfg_path) + + call_list.append("--shape") + + for dim in shape[1:]: + call_list.append(str(dim)) + + call_list.append("--work-dir") + call_list.append(work_dir_path) + subprocess.call(call_list) + +# results path is the parent folder of the modeldirs +def collect_and_organize_all_data( + results_path +): + # organize and plot clutterdata per model + for model_dir in os.listdir(results_path): + if model_dir == "data": + continue + model_path = os.path.join(results_path, model_dir) + if not os.path.isdir(model_path): + continue + clutter_path = os.path.join(model_path, "clutter") + if not os.path.exists(clutter_path): + continue + model_name = model_dir.split("_")[0] + call_list = [ + "python", + "my_projects/scripts/organise_clutter_results.py", + clutter_path + ] + subprocess.call(call_list) + + + # plot clutterdata global and per model + work_dir_name = results_path.split("/")[-2 if results_path[-1] == '/' else -1] + data_path = os.path.join(results_path, "data") + if not os.path.exists(data_path): + os.mkdir(data_path) + call_list = [ + "python", + "my_projects/scripts/plot_clutter_data.py", + results_path, + data_path, + "--global_plot", + '--per_model' + ] + subprocess.call(call_list) + + # generate jsons + call_list = [ + "python", + "my_projects/scripts/generate_json_all_test_results.py", + "-dsp", + results_path, + "-si" + ] + subprocess.call(call_list) + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + 'models_path', + type=str + ) + parser.add_argument( + 'test_results_path', + type=str + ) + args = parser.parse_args() + return args + +def main(): + # selection_path = "my_projects/best_models/selection_trained/arid20_cat" + # test_results_path = "my_projects/test_results/arid20_cat" + args = parse_args() + selection_path = args.models_path + test_results_path = args.test_results_path + for model_name in os.listdir(selection_path): + if model_name == "data": + continue + + cfg_path = os.path.join( + selection_path, + model_name, + f"{model_name}.py" + ) + + + checkpoint_path = os.path.join( + selection_path, + model_name, + "weights.pth" + ) + + + work_dir_path = os.path.join( + test_results_path, + model_name + ) + + prediction_result_path = os.path.join( + test_results_path, + model_name, + "pred_results" + ) + + + show_dir_path = os.path.join( + test_results_path, + model_name, + "show" + ) + + run_performance_test( + cfg_path=cfg_path, + checkpoint_path=checkpoint_path, + work_dir_path=work_dir_path, + prediction_result_path=prediction_result_path, + show_dir_path=show_dir_path + ) + + run_clutter_test( + cfg_path=cfg_path, + checkpoint_path=checkpoint_path, + work_dir_path=work_dir_path + ) + # run_scene_test( + # cfg_path=cfg_path, + # checkpoint_path=checkpoint_path, + # work_dir_path=work_dir_path + # ) + bench_work_dir_path = os.path.join(work_dir_path, "benchmark") + + run_benchmark( + cfg_path=cfg_path, + checkpoint_path=checkpoint_path, + work_dir_path=bench_work_dir_path, + repeat_times=10 + ) + + confusion_matrix_save_path = os.path.join( + work_dir_path, + "confusion_matrix" + ) + + run_confusion_matrix( + cfg_path=cfg_path, + prediction_result_path=prediction_result_path, + confusion_matrix_save_path=confusion_matrix_save_path + ) + + json_path = os.path.join( + confusion_matrix_save_path, + "confusion.json" + ) + + run_confusion_analysis( + json_path=json_path, + save_dir_path=confusion_matrix_save_path, + top_n=10 + ) + + save_flops_file_path = os.path.join( + work_dir_path, + "flops" + ) + + run_flops( + cfg_path=cfg_path, + work_dir_path=save_flops_file_path, + shape=[3, 512, 512] + ) + collect_and_organize_all_data(results_path=test_results_path) + + + +if __name__ == '__main__': + main() + + diff --git a/tools/train_selection.py b/tools/train_selection.py new file mode 100644 index 0000000000..d3e70d6124 --- /dev/null +++ b/tools/train_selection.py @@ -0,0 +1,38 @@ +import subprocess +import os +import torch +import argparse + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + 'selection_path', + type=str + ) + args = parser.parse_args() + return args + +def main(): + args = parse_args() + config_dir = args.selection_path + for cfg_name in os.listdir(config_dir): + # if "mask" not in cfg_name: + # continue + torch.cuda.empty_cache() + + cfg_path = os.path.join(config_dir, cfg_name) + print(f"cfg: {cfg_name}") + call_list = [ + "python3", + "tools/train.py", + cfg_path + ] + subprocess.call(call_list) + + + + +if __name__ == '__main__': + main() + + diff --git a/tools/train_set/__init__.py b/tools/train_set/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tools/train_set/argument_handler.py b/tools/train_set/argument_handler.py new file mode 100644 index 0000000000..c3f7737767 --- /dev/null +++ b/tools/train_set/argument_handler.py @@ -0,0 +1,137 @@ +from config_data_helper import ConfigDataHelper as CDHelper +from dict_utils import TrimData, dataset_info +from config_build_data import ConfigBuildData +from yaml import YAMLError +import os + +class ArgumentHandler: + + @staticmethod + def _generate_config_build_data_list(args) -> list: + config_build_data_list = [] + config_names = [] + method_list = ArgumentHandler._get_method_list_from_args(args=args) + for method_dict in method_list: + method_name = method_dict["method"] + for model_dict in method_dict["models"]: + # if [invalid_entry for key, invalid_entry + # in model_dict.items() if invalid_entry is None]: + # continue + checkpoint_paths = ArgumentHandler._get_checkpoint_paths( + args=args, model_dict=model_dict + ) + for checkpoint_path in checkpoint_paths: + cfg_name = ArgumentHandler._generate_config_name( + args=args, + method_name=method_name, + pretrained=bool(checkpoint_path), + pretrain_data=model_dict["train_data"] + ) + if cfg_name in config_names: + continue + if args.unique and cfg_name in os.listdir("work_dirs"): + continue + config_build_data = ConfigBuildData._get_cfg_build_data( + cfg_name=cfg_name, + base_cfg_path=model_dict["cfg_path"], + dataset_cfg_path=dataset_info[args.dataset]["cfg_path"], + num_classes=dataset_info[args.dataset]["num_classes"], + pretrained=bool(checkpoint_path), + checkpoint_path=model_dict["checkpoint_path"], + pretrain_dataset=model_dict["train_data"], + save_best=args.save_best, + save_interval=args.save_interval, + val_interval=args.val_interval, + batch_size=args.batch_size, + crop_size=args.crop_size, + iterations=args.iterations, + epochs=args.epochs, + dataset_name=args.dataset + ) + config_build_data_list.append(config_build_data) + config_names.append(config_build_data["cfg_name"]) + + + return config_build_data_list + + + + @staticmethod + def _get_method_list_from_args(args) -> list: + project_list = args.projects + method_list = [] + for project_name in project_list: + + metafile_dict = CDHelper._read_metafile(project_name=project_name) + if not metafile_dict: + continue + model_list = CDHelper._extract_model_list( + metafile_dict=metafile_dict + ) + method_list_ = CDHelper._group_models_by_method( + model_list=model_list + ) + if args.trim_method_list: + (accepted_dataset_list, excluded_method_names) = TrimData._get_exclusion_lists_from_args(args=args) + train_data_list = CDHelper._get_train_data_in_method_list(method_list=method_list_) + train_data_list_exclude = [train_data for train_data in train_data_list + if train_data not in accepted_dataset_list] + + method_list_ = CDHelper._method_list_trimmed_by_train_data( + method_list=method_list_, + excluded_datasets=train_data_list_exclude + ) + method_list_ = CDHelper._method_list_trimmed_by_best_train_data( + method_list=method_list_ + ) + + method_list_ = CDHelper._method_list_trimmed_by_method_names( + method_list=method_list_, + excluded_method_names=excluded_method_names + ) + + + method_list += method_list_ + return method_list + + @staticmethod + def _generate_config_name( + args, method_name: str, + pretrained: bool, pretrain_data: str + ) -> str: + + + crop_size = args.crop_size + crop_str = f'{crop_size[0]}x{crop_size[1]}' + + dataset_str = f'{args.dataset.lower().replace(" ", "")}-{crop_str}' + train_settings_str = ArgumentHandler._generate_train_settings_str( + args=args, pretrained=pretrained, pretrain_data=pretrain_data + ) + return f'{method_name}_{train_settings_str}_{dataset_str}' + + @staticmethod + def _generate_train_settings_str( + args, pretrained: bool, pretrain_data: str + ) -> str: + if args.iterations: + iters = int(args.iterations) + iter_str = str(iters) + if iters >= 1000: + iters /= 1000 + iter_str = f'{int(iters)}k' + if args.epochs: + iter_str = f'{int(args.epochs)}epochs' + train_str = f'{args.n_gpus}xb{args.batch_size}' + if pretrained: + train_str += f'-pre-{pretrain_data.lower().replace(" ", "")}' + return f'{train_str}-{iter_str}' + + @staticmethod + def _get_checkpoint_paths(args, model_dict: dict) -> list: + checkpoint_paths = [] + if args.scratch: + checkpoint_paths.append(None) + if args.pretrained: + checkpoint_paths.append(model_dict["checkpoint_path"]) + return checkpoint_paths \ No newline at end of file diff --git a/tools/train_set/cfg_dict_generator.py b/tools/train_set/cfg_dict_generator.py new file mode 100644 index 0000000000..733687a2ab --- /dev/null +++ b/tools/train_set/cfg_dict_generator.py @@ -0,0 +1,471 @@ +import copy +from mmengine.config import Config, ConfigDict +import dict_utils +from copy import deepcopy +import os + + +class ConfigDictGenerator: + def __init__(self) -> None: + self.config_bases = dict_utils.config_bases + self.files_lookup_dict = dict_utils.method_files + self.dataset_info = dict_utils.dataset_info + self.irrelevant_arg_names = [ + "models", "checkpoint", + "config", "verbose" + ] + + def generate_config_build_data_list(self, args) -> list: + config_data = [] + for base_name, settings in self.config_bases.items(): + if base_name not in args.models: + continue + for algorithm_name in settings["algorithm_names"]: + for backbone_name in settings["backbones"]: + method_name = f'{algorithm_name}_{backbone_name}' + if method_name not in self.files_lookup_dict.keys(): + continue + method_data = self.files_lookup_dict[method_name] + checkpoints = self.get_checkpoints(args, method_data) + for checkpoint in checkpoints: + train_dict = self.generate_train_dict_from_args( + args=args, checkpoint=checkpoint + ) + + cfg_name = self.generate_config_name( + train_dict=train_dict, method_name=method_name + ) + if args.unique and cfg_name in os.listdir("work_dirs"): + continue + + cfg_build_data = self.cfg_build_data_from_train_dict( + train_dict=train_dict, cfg_name=cfg_name, + base_cfg_path=method_data["base_file_path"] + ) + config_data.append(cfg_build_data) + return config_data + + def generate_train_dict_from_args(self, args, checkpoint: dict) -> dict: + train_dict = dict() + for arg_name, val in args.__dict__.items(): + if arg_name in self.irrelevant_arg_names: + continue + train_dict[arg_name] = val + train_dict["checkpoint"] = checkpoint + train_dict["pretrained"] = checkpoint is not None + return train_dict + + + + def generate_config_name(self, train_dict: dict, method_name: str) -> str: + train_settings_str = self.generate_train_settings_str( + train_dict=train_dict + ) + crop_size = train_dict["crop_size"] + crop_str = f'{crop_size[0]}x{crop_size[1]}' + + dataset_str = f'{train_dict["dataset"]}-{crop_str}' + + return f'{method_name}_{train_settings_str}_{dataset_str}' + + def generate_train_settings_str(self, train_dict: dict) -> str: + if train_dict["iterations"]: + iters = int(train_dict["iterations"]) + iter_str = str(iters) + if iters >= 1000: + iters /= 1000 + iter_str = f'{int(iters)}k' + if train_dict["epochs"]: + iter_str = f'{int(train_dict["epochs"])}epochs' + train_str = f'{train_dict["n_gpus"]}xb{train_dict["batch_size"]}' + if train_dict["checkpoint"] is not None: + train_str += f'-pre-{train_dict["checkpoint"]["dataset_name"]}' + return f'{train_str}-{iter_str}' + + def cfg_build_data_from_train_dict( + self, train_dict: dict, + cfg_name: str, base_cfg_path: str + ): + cfg_build_data = copy.deepcopy(train_dict) + cfg_build_data["cfg_name"] = cfg_name + cfg_build_data["base_cfg_path"] = base_cfg_path + return cfg_build_data + + def get_all_configs(self, args) -> list: + cfg_build_data_list = self.generate_config_build_data_list(args=args) + cfg_list = [] + for cfg_build_data in cfg_build_data_list: + cfg_list.append( + self.generate_config_from_build_data( + cfg_build_data=cfg_build_data + ) + ) + return cfg_list + + + def generate_config_from_build_data(self, cfg_build_data) -> Config: + ConfigDictGenerator._generate_config_from_build_data( + cfg_build_data=cfg_build_data + ) + + @staticmethod + def _generate_config_from_build_data(cfg_build_data: dict) -> Config: + base_cfg = Config.fromfile(filename=cfg_build_data["base_cfg_path"]) + new_cfg_dict = base_cfg.to_dict() + new_cfg = Config(cfg_dict=new_cfg_dict) + + additional_configs = [] + + ConfigDictGenerator.apply_dataset( + cfg_build_data=cfg_build_data, + new_cfg=new_cfg + ) + + if cfg_build_data["iterations"]: + param_scheduler = new_cfg["param_scheduler"] + # update param scheduler + iteration = 0 + iterations_per_step = int( + cfg_build_data["iterations"] // len(param_scheduler) + ) + for schedule in param_scheduler: + schedule["begin"] = iteration + schedule["end"] = iteration + iterations_per_step + iteration += iterations_per_step + param_scheduler[-1]["end"] = cfg_build_data["iterations"] + # # TODO temp + # param_scheduler = [ + # dict( + # begin=0, by_epoch=False, end=cfg_build_data["iterations"], + # start_factor=1.0, end_factor = 0.01, + # type='LinearLR' + # ) + # ] + # new_cfg["param_scheduler"] = param_scheduler + + + additional_configs.append( + dict( + train_cfg = dict( + max_iters = cfg_build_data["iterations"] + ) + ) + ) + if cfg_build_data["epochs"]: + # update param scheduler + param_scheduler = new_cfg["param_scheduler"] + epoch = 0 + epoch_per_step = int( + cfg_build_data["epochs"] // len(param_scheduler) + ) + for schedule in param_scheduler: + schedule["begin"] = epoch + schedule["end"] = epoch + epoch_per_step + schedule["by_epoch"] = True + epoch += epoch_per_step + param_scheduler[-1]["end"] = cfg_build_data["epochs"] + + + additional_configs.append( + dict( + train_dataloader = dict( + sampler = dict( + type = "DefaultSampler", + shuffle = True + ), + drop_last = True + ) + ) + ) + additional_configs.append( + dict( + train_cfg = dict( + max_epochs = cfg_build_data["epochs"], + type = "EpochBasedTrainLoop" + ) + ) + ) + + train_cfg = dict( + max_epochs = cfg_build_data["epochs"], + type = "EpochBasedTrainLoop" + ) + + new_cfg["train_cfg"] = train_cfg + + additional_configs.append( + dict( + default_hooks = dict( + checkpoint = dict( + by_epoch = True + ), + logger = dict( + log_metric_by_epoch = True + ) + ) + ) + ) + + additional_configs.append( + dict( + log_processor = dict( + by_epoch = True + ) + ) + ) + + + + if cfg_build_data["pretrained"] and cfg_build_data["checkpoint_path"]: + additional_configs.append( + dict( + load_from = cfg_build_data["checkpoint_path"] + ) + ) + if cfg_build_data["save_best"]: + additional_configs.append( + dict( + default_hooks = dict( + checkpoint = dict( + save_best = 'mIoU' + ) + ) + ) + ) + + if cfg_build_data["save_interval"]: + additional_configs.append( + dict( + default_hooks = dict( + checkpoint=dict( + interval=cfg_build_data["save_interval"] + ) + ) + ) + ) + if cfg_build_data["val_interval"]: + additional_configs.append( + dict( + train_cfg = dict( + val_interval=cfg_build_data["val_interval"] + ) + ) + ) + + if cfg_build_data["batch_size"]: + additional_configs.append( + dict( + train_dataloader = dict( + batch_size=cfg_build_data["batch_size"] + ) + ) + ) + if cfg_build_data["crop_size"]: + ConfigDictGenerator._apply_crop_size( + additional_configs=additional_configs, + cfg_build_data=cfg_build_data, + new_cfg=new_cfg + ) + + for additional_cfg in additional_configs: + new_cfg.merge_from_dict(additional_cfg) + + return new_cfg + + + + + @staticmethod + def _get_cfg_build_data( + cfg_name: str, base_cfg_path: str, pretrained: bool, + checkpoint: dict, save_best: bool, save_interval: int, + val_interval: int, batch_size: int, crop_size: int, + iterations: int, epochs: int, dataset_name: str + )-> dict: + return { + "cfg_name" : cfg_name, + "base_cfg_path" : base_cfg_path, + "pretrained" : pretrained, + "checkpoint" : checkpoint, + "save_best" : save_best, + "save_interval" : save_interval, + "val_interval" : val_interval, + "batch_size" : batch_size, + "crop_size" : crop_size, + "iterations" : iterations, + "epochs" : epochs, + "dataset" : dataset_name + } + + @staticmethod + def _apply_crop_size( + additional_configs: list, + cfg_build_data: dict, new_cfg: Config + ): + crop_size = cfg_build_data["crop_size"] + data_preprocessor = dict(size=crop_size) + # Load existing trainpipeline and change + train_pipeline = new_cfg["train_pipeline"] + previous_crop_size = None + for step_dict in train_pipeline: + if step_dict["type"] == "RandomCrop": + previous_crop_size = step_dict["crop_size"] + step_dict["crop_size"] = crop_size + if step_dict["type"] == "RandomResize": + step_dict["scale"] = (2048, min(crop_size)) + if step_dict["type"] == "RandomChoiceResize": + step_dict["scales"] = [int(min(crop_size) * x * 0.1) for x in range(5, 21)] + step_dict["max_size"] = 2048 + additional_configs.append( + dict( + train_pipeline=train_pipeline + ) + ) + additional_configs.append( + dict( + train_dataloader = dict( + dataset = dict( + pipeline=train_pipeline + ) + ) + ) + ) + + test_pipeline = new_cfg["test_pipeline"] + for step_dict in test_pipeline: + if step_dict["type"] == "Resize": + step_dict["scale"] = (2048, min(crop_size)) + if step_dict["type"] == "RandomCrop": + step_dict["crop_size"] = crop_size + if step_dict["type"] == "RandomResize": + step_dict["scale"] = (2048, min(crop_size)) + if step_dict["type"] == "RandomChoiceResize": + step_dict["scales"] = [int(min(crop_size) * x * 0.1) for x in range(5, 21)] + step_dict["max_size"] = 2048 + additional_configs.append( + dict( + test_pipeline=test_pipeline + ) + ) + additional_configs.append( + dict( + test_dataloader = dict( + dataset = dict( + pipeline=test_pipeline + ) + ) + ) + ) + + additional_configs.append( + dict( + val_pipeline=test_pipeline + ) + ) + additional_configs.append( + dict( + val_dataloader = dict( + dataset = dict( + pipeline=test_pipeline + ) + ) + ) + ) + + additional_configs.append( + dict( + crop_size=crop_size + ) + + + ) + additional_configs.append( + dict( + data_preprocessor=data_preprocessor + ) + + + ) + additional_configs.append( + dict( + model=dict( + data_preprocessor=data_preprocessor + ) + + ) + ) + + # Only few models e.g. convnext have need for this + + if "test_cfg" in new_cfg["model"].keys(): + if "crop_size" in new_cfg["model"]["test_cfg"]: + new_cfg["model"]["test_cfg"]["crop_size"] = crop_size + + + # TODO hardcoded now but with search can be generalized + if "backbone" in new_cfg["model"].keys(): + if "img_size" in new_cfg["model"]["backbone"].keys(): + new_cfg["model"]["backbone"]["img_size"] = crop_size + if "image_encoder" in new_cfg["model"].keys(): + if "img_size" in new_cfg["model"]["image_encoder"].keys(): + new_cfg["model"]["image_encoder"]["img_size"] = crop_size + + if cfg_build_data["pretrained"] and previous_crop_size: + if "backbone" in new_cfg["model"].keys(): + if "pretrain_img_size" in new_cfg["model"]["backbone"].keys(): + new_cfg["model"]["backbone"]["pretrain_img_size"] = previous_crop_size + if "image_encoder" in new_cfg["model"].keys(): + if "img_size" in new_cfg["model"]["image_encoder"].keys(): + new_cfg["model"]["image_encoder"]["pretrain_img_size"] = previous_crop_size + + # TODO maybe preserve specific train_pipelines + @staticmethod + def apply_dataset(cfg_build_data: dict, new_cfg: Config): + dataset_info = dict_utils.dataset_info[cfg_build_data["dataset"]] + num_classes = dataset_info["num_classes"] + class_weight = dataset_info["class_weight"] + if "mask" in cfg_build_data["cfg_name"] and "former" in cfg_build_data["cfg_name"]: + class_weight = [0.1] + ([1.0] * num_classes) + dataset_cfg = Config.fromfile( + dataset_info["cfg_path"] + ) + for key, value in dataset_cfg.items(): + new_cfg[key] = value + dict_utils.BFS_change_key( + cfg=new_cfg, + target_key="num_classes", + new_value=num_classes + ) + dict_utils.BFS_change_key( + cfg=new_cfg, + target_key="class_weight", + new_value=class_weight + ) + # dict_utils.BFS_change_key( + # cfg=new_cfg, + # target_key="size_divisor", + # new_value=None + # ) + + # for component_name, component in new_cfg["model"].items(): + # if component_name == "num_classes": + # component = num_classes + # if type(component) is ConfigDict: + # if "num_classes" in component.keys(): + # component["num_classes"] = num_classes + # if type(component) is list: + # for element in component: + # if type(element) is ConfigDict: + # if "num_classes" in element.keys(): + # element["num_classes"] = num_classes + + + + + + def generate_config_names_list(self, args) -> list: + + return [build_item["cfg_name"] for build_item in self.generate_config_build_data_list(args=args)] + + + \ No newline at end of file diff --git a/tools/train_set/config_build_data.py b/tools/train_set/config_build_data.py new file mode 100644 index 0000000000..ef811323e3 --- /dev/null +++ b/tools/train_set/config_build_data.py @@ -0,0 +1,39 @@ + +class ConfigBuildData: + + def _get_empty_cfg_build_data() -> dict: + return ConfigBuildData._get_cfg_build_data( + cfg_name=None, base_cfg_path=None, dataset_cfg_path=None, + num_classes=None, + checkpoint_path=None, pretrain_dataset=None, + save_best=None, save_interval=None, val_interval=None, + batch_size=None, crop_size=None, iterations=None, + epochs=None, dataset_name=None + ) + + @staticmethod + def _get_cfg_build_data( + cfg_name: str, base_cfg_path: str, dataset_cfg_path: str, + num_classes: int, + pretrained: bool, checkpoint_path: str, pretrain_dataset: str, + save_best: bool, save_interval: int, + val_interval: int, batch_size: int, crop_size: int, + iterations: int, epochs: int, dataset_name: str + )-> dict: + return { + "cfg_name" : cfg_name, + "base_cfg_path" : base_cfg_path, + "dataset_cfg_path" : dataset_cfg_path, + "num_classes" : num_classes, + "pretrained" : pretrained, + "checkpoint_path" : checkpoint_path, + "pretrain_dataset" : pretrain_dataset, + "save_best" : save_best, + "save_interval" : save_interval, + "val_interval" : val_interval, + "batch_size" : batch_size, + "crop_size" : crop_size, + "iterations" : iterations, + "epochs" : epochs, + "dataset" : dataset_name + } \ No newline at end of file diff --git a/tools/train_set/config_data_helper.py b/tools/train_set/config_data_helper.py new file mode 100644 index 0000000000..deeb953a59 --- /dev/null +++ b/tools/train_set/config_data_helper.py @@ -0,0 +1,276 @@ +import yaml +import os +from pathlib import Path +from typing import Union +from dict_utils import DEFAULT_CONFIG_ROOT_PATH +import re +import dict_utils + +class ConfigDataHelper: + """_summary_ + reads and handles yaml metafiles + """ + + @staticmethod + def _generate_available_project_list( + config_root_path: Union[str, Path] = DEFAULT_CONFIG_ROOT_PATH + ) -> list: + """generates list of available projects + + Args: + config_root_path (Union[str, Path], optional): + Defaults to DEFAULT_CONFIG_ROOT_PATH. + + Returns: + list: list of available projects + """ + + return [ + project for project in os.listdir(config_root_path) + if not project == "_base_" + ] + + # take out all irrelevant fields + @staticmethod + def _extract_model_list(metafile_dict: dict) -> list: + + def _extract_method_name(cfg_name): + idx = cfg_name.find("xb") + return cfg_name[:idx - 2] + + def _extract_crop_size(cfg_name): + + split = re.split("_|-", cfg_name) + crops = [crop for crop in split + if len(crop.split('x')) == 2 + and crop.split('x')[0].isdigit() + and crop.split('x')[1].isdigit()] + if not crops: + return None + crop_str = crops[0] + return (int(crop_str.split('x')[0]), int(crop_str.split('x')[1])) + + model_list = [] + if "Models" not in metafile_dict.keys(): + return [] + for model_dict in metafile_dict["Models"]: + method_name = _extract_method_name( + cfg_name=model_dict["Name"] + ) + crop_size = _extract_crop_size( + cfg_name=model_dict["Name"] + ) + if "mIoU" in model_dict["Results"]["Metrics"].keys(): + mIoU = model_dict["Results"]["Metrics"]["mIoU"] + else: + # print( + # f'{model_dict["Name"]} : no MIoU, using {model_dict["Results"]["Metrics"]} instead' + # ) + mIoU = list(model_dict["Results"]["Metrics"])[0][1] + model_list.append( + { + "name" : model_dict["Name"], + "cfg_path" : model_dict["Config"], + "train_data" : model_dict["Metadata"]["Training Data"], + "method" : method_name, + "crop_size" : crop_size, + "checkpoint_path" : model_dict["Weights"], + "mIoU" : mIoU + } + ) + return model_list + + @staticmethod + def _group_models_by_method(model_list: list) -> list: + + def _method_of_model_in_list( + method_list: list, model_dict: dict + ) -> bool: + for method_dict in method_list: + if _method_equal( + method_dict=method_dict, model_dict=model_dict + ): + return True + return False + + def _method_equal(method_dict: dict, model_dict: dict) -> bool: + return method_dict["method"] == model_dict["method"] + + def _model_dict_equal(model_dict1: dict, model_dict2: dict) -> bool: + method_eq = model_dict1["method"] == model_dict2["method"] + data_eq = model_dict1["train_data"] == model_dict2["train_data"] + return method_eq and data_eq + + def _find_model_in_method_dict( + model_dict: dict, method_dict: dict + ) -> dict: + for model_dict_ in method_dict["models"]: + if _model_dict_equal( + model_dict1=model_dict, + model_dict2=model_dict_ + ): + return model_dict_ + + return {} + + + # best mIoU for now + def _get_best_variant(model_dict1: dict, model_dict2: dict) -> dict: + if model_dict1["mIoU"] > model_dict2["mIoU"]: + return model_dict1 + return model_dict2 + + def _add_new_model_to_method_dict( + model_dict: dict, method_dict: dict + ) -> None: + method_dict["models"].append(model_dict) + + def _replace_model_in_method_dict( + new_model_dict: dict, old_model_dict: dict, + method_dict: dict + ) -> None: + idx = method_dict["models"].index(old_model_dict) + method_dict["models"][idx] = new_model_dict + + def _generate_new_method_dict(model_dict: dict) -> dict: + return { + "method" : model_dict["method"], + "models" : [model_dict] + } + + method_list = [] + for model_dict in model_list: + + + if not _method_of_model_in_list( + method_list=method_list, + model_dict=model_dict + ): + new_method_dict = _generate_new_method_dict( + model_dict=model_dict + ) + method_list.append(new_method_dict) + continue + for method_dict in method_list: + # method found in preexisting list + if _method_equal( + method_dict=method_dict, model_dict=model_dict + ): + + model_ret = _find_model_in_method_dict( + model_dict=model_dict, + method_dict=method_dict + ) + + # if there is already such a model + if model_ret: + best_model = _get_best_variant( + model_dict1=model_dict, + model_dict2=model_ret + ) + + _replace_model_in_method_dict( + new_model_dict=best_model, + old_model_dict=model_ret, + method_dict=method_dict + ) + else: + _add_new_model_to_method_dict( + model_dict=model_dict, + method_dict=method_dict + ) + return method_list + @staticmethod + def _method_list_trimmed_by_best_train_data( + method_list: list + ): + def get_best_option(model_dict1, model_dict2): + ranking = dict_utils.TrimData.accepted_dataset_list + dataset1 = model_dict1["train_data"] + dataset2 = model_dict2["train_data"] + if ranking.index(dataset1) <= ranking.index(dataset2): + return model_dict1 + return model_dict2 + + for method_dict in method_list: + if not method_dict["models"]: + continue + best_model_dict = method_dict["models"][0] + for model_dict in method_dict["models"][1:]: + best_model_dict = get_best_option(best_model_dict, model_dict) + + method_dict["models"] = [best_model_dict] + return method_list + @staticmethod + def _method_list_trimmed_by_model_names( + method_list: list, excluded_names: list + ) -> list: + for method_dict in method_list: + new_model_list = [] + for model_dict in method_dict["models"]: + if not model_dict["name"] in excluded_names: + new_model_list.append(model_dict) + + method_dict["models"] = new_model_list + return method_list + + @staticmethod + def _method_list_trimmed_by_train_data( + method_list: list, excluded_datasets: list + ) -> list: + for method_dict in method_list: + new_model_list = [] + for model_dict in method_dict["models"]: + if not model_dict["train_data"] in excluded_datasets: + new_model_list.append(model_dict) + method_dict["models"] = new_model_list + return method_list + + @staticmethod + def _method_list_trimmed_by_method_names( + method_list: list, excluded_method_names: list + ) -> list: + new_method_list = [] + for method_dict in method_list: + if not method_dict["method"] in excluded_method_names: + new_method_list.append(method_dict) + return new_method_list + + + + @staticmethod + def _read_metafile( + project_name: str, + config_root_path: Union[str, Path] = DEFAULT_CONFIG_ROOT_PATH + ) -> dict: + """reads metafile.yaml in a safe way + + Args: + project_name (str): + name of project to which metafile belongs + config_root_path (Union[str, Path], optional): + Defaults to DEFAULT_CONFIG_ROOT_PATH. + + Returns: + dict: contents of metafile + """ + path = os.path.join(config_root_path, project_name, "metafile.yaml") + if not os.path.exists(path): + print(f'skipping load: path does not exist: {path}') + return {} + with open(path, "r") as f: + try: + metafile_dict = yaml.safe_load(f) + except yaml.YAMLError as exc: + print(exc) + return {} + return metafile_dict + + @staticmethod + def _get_train_data_in_method_list(method_list: list) -> list: + data_list = [] + for method_dict in method_list: + for model_dict in method_dict["models"]: + if not model_dict["train_data"] in data_list: + data_list.append(model_dict["train_data"]) + return data_list \ No newline at end of file diff --git a/tools/train_set/dict_utils.py b/tools/train_set/dict_utils.py new file mode 100644 index 0000000000..07de119ae4 --- /dev/null +++ b/tools/train_set/dict_utils.py @@ -0,0 +1,218 @@ + +import yaml +import os +from pathlib import Path +from typing import Union +from mmengine import ConfigDict, Config +import queue as queue_ +import copy + +DEFAULT_CONFIG_ROOT_PATH = "/media/ids/Ubuntu files/mmsegmentation/configs" +BATCH_SIZE_DEFAULT = 2 +N_GPU_DEFAULT = 1 +VAL_INTERVAL_EPOCH_DEFAULT = 1 +VAL_INTERVAL_ITERATIONS_DEFAULT = 1000 +N_ITERATIONS_DEFAULT = 20000 + +dataset_info = { + "hots-v1" : + { + "cfg_path" : "configs/_base_/datasets/hots_v1_640x480.py", + "num_classes" : 47, + "class_weight" : None + }, + "irl_vision_sim" : + { + "cfg_path" : "configs/_base_/datasets/irl_vision_sim_640x480.py", + "num_classes" : 72, + "class_weight" : None + } +} + +alias_dict = { + +} + + +class TrimData: + + + accepted_dataset_list = [ + "COCO-Stuff 164k", + "COCO-Stuff 10k", + "ADE20K", + "Pascal VOC 2012 + Aug", + "Pascal Context", + "Pascal Context 59", + "Cityscapes" + ] + + excluded_method_names = [ + "ann_r101-d8", + "apcnet_r101-d8", + "beit-base_upernet", + "beit-large_upernet", + "bisenetv1_r18-d32", # only take pretrained vars (see repo) + "bisenetv1_r50-d32", + "bisenetv1_r101-d32", + "ccnet_r101-d8", + "convnext-large_upernet", + "convnext-xlarge_upernet", + "danet_r101-d8", + "deeplabv3_r18b-d8", # take out b versions due to redundancy + "deeplabv3_r50b-d8", + "deeplabv3_r101b-d8", + "deeplabv3_r101-d8", + # "deeplabv3plus_r50-d8", + "deeplabv3plus_r50b-d8", + "deeplabv3plus_r101-d8", + "deeplabv3plus_r101b-d8", + "deeplabv3plus_r101-d16-mg124", + "ddeeplabv3plus_r101-d16-mg124", + "deeplabv3plus_r18b-d8", + "dmnet_r50-d8", + "dmnet_r101-d8", + "dnl_r50-d8", + "dnl_r101-d8", + "dpt_vit-b16", + "emanet_r50-d8", + "eemanet_r50-d8", + "emanet_r101-d8", + "encnet_r50-d8", + "encnet_r101-d8", + "fastfcn_r50-d32_jpu_enc", + "fcn_r18b-d8", # for these 3 I ignore the worst of the options (b/no b) + "fcn_r50-d8", + "fcn_r101-d8", + "fcn_r101b-d8", + "fcn-d6_r50b", # best of b or !b + "fcn-d6_r101b", + "gcnet_r101-d8", + "icnet_r18-d8", # better options are pretrained + "icnet_r50-d8", + "icnet_r101-d8", + "isanet_r50-d8", + "isanet_r101-d8", + "knet-s3_swin-l_upernet", + "mae-base_upernet", + "mask2former_swin-s", + "mask2former_swin-b-in22k-384x384-pre", + "mask2former_swin-l-in22k-384x384-pre", + "mask2former_swin-b-in1k-384x384-pre", + "maskformer_swin-s_upernet", + "mobilenet-v3-d8-scratch_lraspp", + "mobilenet-v3-d8-scratch-s_lraspp", + "nonlocal_r101-d8", + "ocrnet_hr48", + "pidnet-s", + "pidnet-m", + "pidnet-l", + "psanet_r101-d8", + "pspnet_r18b-d8", + "pspnet_r50b-d8", + "pspnet_r101-d8", + "pspnet_r101b-d8", + "resnest_s101-d8_fcn", + "resnest_s101-d8_pspnet", + "resnest_s101-d8_deeplabv3", + "resnest_s101-d8_deeplabv3plus", + "san-vit-b16_coco-stuff164k-640x", + "san-vit-l14_coco-stuff164k-640x", + "segformer_mit-b4", + "segformer_mit-b5", + "segmenter_vit-l_mask", + "segnext_mscan-l", + "setr_vit-l_naive", + "setr_vit-l_pup", + "setr_vit-l_mla", + "setr_vit-l-mla", + "swin-base-patch4-window7-in1k-pre_upernet", + "swin-base-patch4-window12-in1k-384x384-pre_upernet", + "stdc1", + "stdc2", + "swin-base-patch4-window7-in22k-pre_upernet", + "swin-base-patch4-window12-in22k-384x384-pre_upernet", + "twins_pcpvt-b_uperhead", + "twins_pcpvt-l_uperhead", + "twins_svt-b_uperhead", + "twins_svt-l_fpn_fpnhead", + "unet-s5-d16_fcn", + "vit_vit-b16_mln_upernet", + "vit_vit-b16-ln_mln_upernet", + "vit_deit-s16_mln_upernet", + "vit_deit-b16_upernet", + "vit_deit-b16_mln_upernet", + "vit_deit-b16-ln_mln_upernet" + + + ] + + @staticmethod + def _get_exclusion_lists_from_args(args) -> tuple: + + accepted_dataset_list_ = copy.deepcopy(TrimData.accepted_dataset_list) + excluded_method_names_ = copy.deepcopy(TrimData.excluded_method_names) + + if args.crop_size == (512, 512): + excluded_method_names_.append("ddrnet_23-slim_in1k-pre") + excluded_method_names_.append("ddrnet_23_in1k-pre") + + return (accepted_dataset_list_, excluded_method_names_) + + +class Vertex: + def __init__(self, item, parent, key = None): + self.item = item + self.parent = parent + self.key = key + + def __eq__(self, value: object) -> bool: + if isinstance(value, self.__class__): + return value.item == self.item and value.parent == self.parent + return False + +# BFS + +def BFS_change_key(cfg, target_key, new_value): + queue = list() + vertex = Vertex(cfg, None) + queue.append(vertex) + while queue: + vertex = queue.pop(0) + children = expand_vertex(vertex) + if vertex.key == target_key: + vertex.parent.item[vertex.key] = new_value + for child in children: + queue.append(child) + + +def expand_vertex(vertex: Vertex) -> list: + children = [] + if type(vertex.item) is Config or type(vertex.item) is ConfigDict or type(vertex.item) is dict: + for key, val in vertex.item.items(): + item_ = vertex.item[key] + children.append( + Vertex( + item=item_, + parent=vertex, + key=key + ) + ) + elif type(vertex.item) is list: + for entry in vertex.item: + children.append( + Vertex( + item=entry, + parent=vertex, + key=None + ) + ) + else: + return [] + + return children + +def is_leaf(vertex: Vertex) -> bool: + return bool(expand_vertex(vertex=vertex)) + + \ No newline at end of file diff --git a/tools/train_set/train_selection.py b/tools/train_set/train_selection.py new file mode 100644 index 0000000000..1e6f136f11 --- /dev/null +++ b/tools/train_set/train_selection.py @@ -0,0 +1,248 @@ +from config_build_data import ConfigBuildData as CFBD +from argument_handler import ArgumentHandler as ArgHand +from dict_utils import dataset_info +import dict_utils +from cfg_dict_generator import ConfigDictGenerator +from config_data_helper import ConfigDataHelper as CDH +import os +from mmengine.config import Config + +from copy import deepcopy +from mmengine.runner import Runner + +from mmseg.registry import RUNNERS +import torch + +_empty_checkpoint = { + "dataset_name" : None, + "checkpoint_path" : None +} +def run_cfg(cfg): + torch.cuda.empty_cache() + # load config + + cfg.launcher = "none" + # work_dir is determined in this priority: CLI > segment in file > filename + + # maybe implement mixed precision training + + # build the runner from config + if 'runner_type' not in cfg: + # build the default runner + runner = Runner.from_cfg(cfg) + else: + # build customized runner from the registry + # if 'runner_type' is set in the cfg + runner = RUNNERS.build(cfg) + # start training + runner.train() + +def train_best( + config_dir_path = "my_projects/configs", +): + + for cfg_name in os.listdir(config_dir_path): + cfg_path = os.path.join(config_dir_path, cfg_name) + cfg = Config.fromfile(cfg_path) + cfg.work_dir = os.path.join('./work_dirs', cfg_name) + + + try: + run_cfg(cfg=cfg) + except: + print("failed") + +def train_my_configs( + config_dir_path = "my_projects/best_models/selection", + datasets = ["irl_vision_sim", "hots-v1"], + crop_size = (512, 512), + iterations = 5000, + save_interval = 1000, + val_interval = 1000, + unique = True, + pretrained = True, + save_best = True, + exclude_names = [] +): + + checkpoint_lookup_table = generate_checkpoint_lookup_by_cfg_name() + + # cfg_names = [ + # "fpn_poolformer_m36_8xb4-40k_ade20k-512x512", + # "knet-s3_r50-d8_fcn_8xb2-adamw-80k_ade20k-512x512", + # "mask2former_r50_8xb2-160k_ade20k-512x512", + # "ocrnet_hr18s_4xb4-40k_voc12aug-512x512", + # "segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512", + # "twins_svt-b_fpn_fpnhead_8xb4-80k_ade20k-512x512" + # ] + expection_log_path = "my_projects/exception_log.log" + for base_cfg_name in os.listdir(config_dir_path): + # if len(b_data["cfg_name"] + # [name for name in exclude_names if name in base_cfg_name] + # ) > 0: + # continue + # for base_cfg_name in ["fcn_hr18s_4xb4-20k_voc12aug-512x512.py"]: + base_cfg_path = os.path.join(config_dir_path, base_cfg_name) + base_cfg_name = base_cfg_path.split("/")[-1].replace(".py", "") + + # if base_cfg_name not in cfg_names: + # continue + # print(base_cfg_name) + for target_dataset in datasets: + checkpoints = [_empty_checkpoint] + # TODO temp + checkpoints = [] + if pretrained: + checkpoints.append( + checkpoint_lookup_table[base_cfg_name] + ) + for checkpoint_dict in checkpoints: + cfg_name = generate_new_cfg_name( + base_cfg_name=base_cfg_name, + dataset_name=target_dataset, + iterations=iterations, + crop_size=crop_size, + pretrain_data=checkpoint_dict["dataset_name"] + ) + if unique and cfg_name in os.listdir("work_dirs"): + continue + b_data = CFBD._get_cfg_build_data( + cfg_name=cfg_name, + base_cfg_path=base_cfg_path, + dataset_cfg_path=dataset_info[target_dataset]["cfg_path"], + num_classes=dataset_info[target_dataset]["num_classes"], + pretrained= checkpoint_dict is not _empty_checkpoint, + checkpoint_path=checkpoint_dict["checkpoint_path"], + pretrain_dataset=checkpoint_dict["dataset_name"], + save_best=save_best, + save_interval=save_interval, + val_interval=val_interval, + batch_size=2, + crop_size=crop_size, + iterations=iterations, + epochs=None, + dataset_name=target_dataset + ) + cfg = ConfigDictGenerator._generate_config_from_build_data(cfg_build_data=b_data) + # change_lr(cfg=cfg, lr=0.01) + cfg.work_dir = os.path.join('./work_dirs', b_data["cfg_name"]) + try: + run_cfg(cfg=cfg) + except Exception as exp: + + print(f"couldn't run config: {b_data['cfg_name']}") + print(f"excep:\n{exp}") + with open(expection_log_path, 'a') as ex_log: + log_line = f"cfg: {b_data['cfg_name']}\nexcept: {exp}" + ex_log.write(log_line) + +def change_lr(cfg, lr = 0.1, weight_decay = 0): + cfg.optimizer["lr"] = lr + cfg.optimizer["weight_decay"] = weight_decay + cfg.optim_wrapper.optimizer["lr"] = lr + cfg.optim_wrapper.optimizer["weight_decay"] = weight_decay + + +def generate_checkpoint_lookup_by_cfg_name(global_config_path = "configs"): + + lookup_table = {} + for project_dir in os.listdir(global_config_path): + meta_dict = CDH._read_metafile( + project_name=project_dir, + config_root_path=global_config_path + ) + model_list = CDH._extract_model_list(metafile_dict=meta_dict) + for model_dict in model_list: + lookup_table[model_dict["name"]] = { + "dataset_name" : model_dict["train_data"], + "checkpoint_path" : model_dict["checkpoint_path"] + } + return lookup_table + +def generate_new_cfg_name( + base_cfg_name : str, + dataset_name : str, + iterations : int, + crop_size : tuple, + pretrain_data : str = "", + device_info : str = "1xb2" +): + # make new name + # basename with split at xb + method_name = base_cfg_name.split("xb")[0][:-2] + + new_name = method_name + f"_{device_info}" + if pretrain_data is not None and pretrain_data != "": + new_name += f'-pre-{pretrain_data.lower().replace(" ", "")}' + new_name += f"-{int(iterations/1000)}k" + new_name += f"_{dataset_name}" + new_name += f"-{crop_size[0]}x{crop_size[1]}" + return new_name + + +def test( + config_dir_path = "configs/my_configs", + datasets = ["irl_vision_sim", "hots-v1"], + crop_size = (512, 512), + iterations = 2000, + save_interval = 500, + val_interval = 500, + unique = True, + pretrained = True +): + + checkpoint_lookup_table = generate_checkpoint_lookup_by_cfg_name() + + for base_cfg_name in os.listdir(config_dir_path): + # for base_cfg_name in ["fcn_hr18s_4xb4-20k_voc12aug-512x512.py"]: + base_cfg_path = os.path.join(config_dir_path, base_cfg_name) + base_cfg_name = base_cfg_path.split("/")[-1].replace(".py", "") + # print(base_cfg_name) + for target_dataset in datasets: + checkpoints = [_empty_checkpoint] + if pretrained: + checkpoints.append( + checkpoint_lookup_table[base_cfg_name] + ) + for checkpoint_dict in checkpoints: + cfg_name = generate_new_cfg_name( + base_cfg_name=base_cfg_name, + dataset_name=target_dataset, + iterations=iterations, + crop_size=crop_size, + pretrain_data=checkpoint_dict["dataset_name"] + ) + if unique and cfg_name in os.listdir("work_dirs"): + continue + b_data = CFBD._get_cfg_build_data( + cfg_name=cfg_name, + base_cfg_path=base_cfg_path, + dataset_cfg_path=dataset_info[target_dataset]["cfg_path"], + num_classes=dataset_info[target_dataset]["num_classes"], + pretrained= checkpoint_dict is not _empty_checkpoint, + checkpoint_path=checkpoint_dict["checkpoint_path"], + pretrain_dataset=checkpoint_dict["dataset_name"], + save_best=False, + save_interval=save_interval, + val_interval=val_interval, + batch_size=2, + crop_size=crop_size, + iterations=iterations, + epochs=None, + dataset_name=target_dataset + ) + cfg = ConfigDictGenerator._generate_config_from_build_data(cfg_build_data=b_data) + cfg.work_dir = os.path.join('./work_dirs', b_data["cfg_name"]) + try: + run_cfg(cfg=cfg) + except: + print(f"couldn't run config: {b_data['cfg_name']}") + + +def main(): + # test() + # train_my_configs() + train_best() + +if __name__ == '__main__': + main() diff --git a/tools/train_set/train_set.py b/tools/train_set/train_set.py new file mode 100644 index 0000000000..fe3a05023a --- /dev/null +++ b/tools/train_set/train_set.py @@ -0,0 +1,200 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import argparse +import logging +import os +import os.path as osp + +from mmengine.config import Config, DictAction +from mmengine.logging import print_log +from mmengine.runner import Runner + +from mmseg.registry import RUNNERS +import torch +import copy +from dict_utils import ( + DEFAULT_CONFIG_ROOT_PATH, BATCH_SIZE_DEFAULT, N_GPU_DEFAULT, + VAL_INTERVAL_EPOCH_DEFAULT, VAL_INTERVAL_ITERATIONS_DEFAULT, + N_ITERATIONS_DEFAULT, dataset_info +) +from cfg_dict_generator import ConfigDictGenerator +from config_data_helper import ConfigDataHelper as CDHelper +from argument_handler import ArgumentHandler + + +def parse_args(): + parser = argparse.ArgumentParser(description='Train a segmentor') + parser.add_argument( + "--batch_size", + type=int, + default=BATCH_SIZE_DEFAULT, + help="train batch size" + ) + + parser.add_argument( + "--crop_size", + type=str, + default="512x512", + help="crop size in #x# format" + ) + parser.add_argument( + "--dataset", + type=str, + default="hots-v1", + choices=list(dataset_info.keys()) + ) + parser.add_argument( + "--epochs", + type=int, + default=None + ) + parser.add_argument( + "--trim_method_list", + action='store_true', + default=False + ) + parser.add_argument( + "--iterations", + type=str, + default=None, + help="iterations in #k format" + ) + parser.add_argument( + "--projects", + nargs='+', + default=list(CDHelper._generate_available_project_list()) + ) + parser.add_argument( + "--n_gpus", + type=int, + default=N_GPU_DEFAULT + ) + parser.add_argument( + "--pretrained", + action='store_true', + default=False + ) + parser.add_argument( + "--save_best", + action='store_true', + default=False + ) + parser.add_argument( + "--save_interval", + type=int, + default=None + ) + parser.add_argument( + "--scratch", + help="from scratch", + action='store_true', + default=False + ) + parser.add_argument( + "--unique", + action='store_true', + default=False + ) + parser.add_argument( + "--val_interval", + type=int, + default=None + ) + parser.add_argument( + "--verbose", + action='store_true', + default=False + ) + + args = parser.parse_args() + + if not args.scratch and not args.pretrained: + args.scratch = True + + args.crop_size = (int(args.crop_size.lower().split('x')[0]), int(args.crop_size.lower().split('x')[1])) + if args.iterations: + args.iterations = int(args.iterations.replace('k', '000')) if type(args.iterations) is str and 'k' in args.iterations else int(args.iterations) + if not args.epochs and not args.val_interval: + args.val_interval = VAL_INTERVAL_ITERATIONS_DEFAULT + + if args.epochs: + args.iterations = None + if not args.val_interval or args.val_interval > args.epochs: + args.val_interval = VAL_INTERVAL_EPOCH_DEFAULT + + if not args.epochs and not args.iterations: + args.iterations = N_ITERATIONS_DEFAULT + return args + +def run_cfg(cfg): + torch.cuda.empty_cache() + # load config + + cfg.launcher = "none" + # work_dir is determined in this priority: CLI > segment in file > filename + + # maybe implement mixed precision training + + # build the runner from config + if 'runner_type' not in cfg: + # build the default runner + runner = Runner.from_cfg(cfg) + else: + # build customized runner from the registry + # if 'runner_type' is set in the cfg + runner = RUNNERS.build(cfg) + # start training + runner.train() + +# TODO +def unique(cfg_build_data_list): + cfg_build_data_list_ = [] + for cfg_bd in cfg_build_data_list: + for cfg_bd_ in cfg_build_data_list_: + if cfg_bd["cfg_name"] == cfg_bd_["cfg_name"]: + continue + + +def main(): + args = parse_args() + if args.verbose: + print(f"args: {args}") + # method_list = ArgumentHandler._get_method_list_from_args(args=args) + # for method in method_list: + # print(method) + + + + cfg_build_data_list = ArgumentHandler._generate_config_build_data_list( + args=args + ) + + print(len(cfg_build_data_list)) + + + if args.verbose: + for cfg_build_data in cfg_build_data_list: + print(cfg_build_data["cfg_name"]) + # print(cfg_build_data) + # exit() + # method_list = ArgumentHandler._get_method_list_from_args(args=args) + # for method in method_list: + # print('#' * 80) + # for key, val in method.items(): + # print(f"{key} : {val}") + # exit() + for cfg_build_data in cfg_build_data_list: + if args.verbose: + print('#' * 80) + print(f'running config: {cfg_build_data["cfg_name"]}') + print('#' * 80) + cfg = ConfigDictGenerator._generate_config_from_build_data(cfg_build_data=cfg_build_data) + + cfg.work_dir = osp.join('./work_dirs', cfg_build_data["cfg_name"]) + try: + run_cfg(cfg=cfg) + except: + print(f"couldn't run config: {cfg_build_data['cfg_name']}") + + +if __name__ == '__main__': + main() \ No newline at end of file