Merge pull request #2264 from huggingface/group_size_eff

Allow group_size override for more efficientnet and mobilenetv3 based…

Author: rwightman

timm/models/efficientnet.py

@@ -488,7 +488,8 @@ def _gen_mnasnet_small(variant, channel_multiplier=1.0, pretrained=False, **kwar
 
 def _gen_mobilenet_v1(
         variant, channel_multiplier=1.0, depth_multiplier=1.0,
-        fix_stem_head=False, head_conv=False, pretrained=False, **kwargs):
+        group_size=None, fix_stem_head=False, head_conv=False, pretrained=False, **kwargs
+):
     """
     Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py
     Paper: https://arxiv.org/abs/1801.04381
@@ -503,7 +504,12 @@ def _gen_mobilenet_v1(
     round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
     head_features = (1024 if fix_stem_head else max(1024, round_chs_fn(1024))) if head_conv else 0
     model_kwargs = dict(
-        block_args=decode_arch_def(arch_def, depth_multiplier=depth_multiplier, fix_first_last=fix_stem_head),
+        block_args=decode_arch_def(
+            arch_def,
+            depth_multiplier=depth_multiplier,
+            fix_first_last=fix_stem_head,
+            group_size=group_size,
+        ),
         num_features=head_features,
         stem_size=32,
         fix_stem=fix_stem_head,
@@ -517,7 +523,9 @@ def _gen_mobilenet_v1(
 
 
 def _gen_mobilenet_v2(
-        variant, channel_multiplier=1.0, depth_multiplier=1.0, fix_stem_head=False, pretrained=False, **kwargs):
+        variant, channel_multiplier=1.0, depth_multiplier=1.0,
+        group_size=None, fix_stem_head=False, pretrained=False, **kwargs
+):
     """ Generate MobileNet-V2 network
     Ref impl: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_v2.py
     Paper: https://arxiv.org/abs/1801.04381
@@ -533,7 +541,12 @@ def _gen_mobilenet_v2(
     ]
     round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
     model_kwargs = dict(
-        block_args=decode_arch_def(arch_def, depth_multiplier=depth_multiplier, fix_first_last=fix_stem_head),
+        block_args=decode_arch_def(
+            arch_def,
+            depth_multiplier=depth_multiplier,
+            fix_first_last=fix_stem_head,
+            group_size=group_size,
+        ),
         num_features=1280 if fix_stem_head else max(1280, round_chs_fn(1280)),
         stem_size=32,
         fix_stem=fix_stem_head,
@@ -613,7 +626,8 @@ def _gen_spnasnet(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
 
 def _gen_efficientnet(
         variant, channel_multiplier=1.0, depth_multiplier=1.0, channel_divisor=8,
-        group_size=None, pretrained=False, **kwargs):
+        group_size=None, pretrained=False, **kwargs
+):
     """Creates an EfficientNet model.
 
     Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py
@@ -661,7 +675,8 @@ def _gen_efficientnet(
 
 
 def _gen_efficientnet_edge(
-        variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, pretrained=False, **kwargs):
+        variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, pretrained=False, **kwargs
+):
     """ Creates an EfficientNet-EdgeTPU model
 
     Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/edgetpu
@@ -692,7 +707,8 @@ def _gen_efficientnet_edge(
 
 
 def _gen_efficientnet_condconv(
-        variant, channel_multiplier=1.0, depth_multiplier=1.0, experts_multiplier=1, pretrained=False, **kwargs):
+        variant, channel_multiplier=1.0, depth_multiplier=1.0, experts_multiplier=1, pretrained=False, **kwargs
+):
     """Creates an EfficientNet-CondConv model.
 
     Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/condconv
@@ -764,7 +780,8 @@ def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0
 
 
 def _gen_efficientnetv2_base(
-        variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
+        variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, pretrained=False, **kwargs
+):
     """ Creates an EfficientNet-V2 base model
 
     Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
@@ -780,7 +797,7 @@ def _gen_efficientnetv2_base(
     ]
     round_chs_fn = partial(round_channels, multiplier=channel_multiplier, round_limit=0.)
     model_kwargs = dict(
-        block_args=decode_arch_def(arch_def, depth_multiplier),
+        block_args=decode_arch_def(arch_def, depth_multiplier, group_size=group_size),
         num_features=round_chs_fn(1280),
         stem_size=32,
         round_chs_fn=round_chs_fn,
@@ -793,7 +810,8 @@ def _gen_efficientnetv2_base(
 
 
 def _gen_efficientnetv2_s(
-        variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, rw=False, pretrained=False, **kwargs):
+        variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, rw=False, pretrained=False, **kwargs
+):
     """ Creates an EfficientNet-V2 Small model
 
     Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
@@ -831,7 +849,9 @@ def _gen_efficientnetv2_s(
     return model
 
 
-def _gen_efficientnetv2_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
+def _gen_efficientnetv2_m(
+        variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, pretrained=False, **kwargs
+):
     """ Creates an EfficientNet-V2 Medium model
 
     Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
@@ -849,7 +869,7 @@ def _gen_efficientnetv2_m(variant, channel_multiplier=1.0, depth_multiplier=1.0,
     ]
 
     model_kwargs = dict(
-        block_args=decode_arch_def(arch_def, depth_multiplier),
+        block_args=decode_arch_def(arch_def, depth_multiplier, group_size=group_size),
         num_features=1280,
         stem_size=24,
         round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
@@ -861,7 +881,9 @@ def _gen_efficientnetv2_m(variant, channel_multiplier=1.0, depth_multiplier=1.0,
     return model
 
 
-def _gen_efficientnetv2_l(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
+def _gen_efficientnetv2_l(
+        variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, pretrained=False, **kwargs
+):
     """ Creates an EfficientNet-V2 Large model
 
     Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
@@ -879,7 +901,7 @@ def _gen_efficientnetv2_l(variant, channel_multiplier=1.0, depth_multiplier=1.0,
     ]
 
     model_kwargs = dict(
-        block_args=decode_arch_def(arch_def, depth_multiplier),
+        block_args=decode_arch_def(arch_def, depth_multiplier, group_size=group_size),
         num_features=1280,
         stem_size=32,
         round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
@@ -891,7 +913,9 @@ def _gen_efficientnetv2_l(variant, channel_multiplier=1.0, depth_multiplier=1.0,
     return model
 
 
-def _gen_efficientnetv2_xl(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
+def _gen_efficientnetv2_xl(
+        variant, channel_multiplier=1.0, depth_multiplier=1.0, group_size=None, pretrained=False, **kwargs
+):
     """ Creates an EfficientNet-V2 Xtra-Large model
 
     Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
@@ -909,7 +933,7 @@ def _gen_efficientnetv2_xl(variant, channel_multiplier=1.0, depth_multiplier=1.0
     ]
 
     model_kwargs = dict(
-        block_args=decode_arch_def(arch_def, depth_multiplier),
+        block_args=decode_arch_def(arch_def, depth_multiplier, group_size=group_size),
         num_features=1280,
         stem_size=32,
         round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
@@ -923,7 +947,8 @@ def _gen_efficientnetv2_xl(variant, channel_multiplier=1.0, depth_multiplier=1.0
 
 def _gen_efficientnet_x(
         variant, channel_multiplier=1.0, depth_multiplier=1.0, channel_divisor=8,
-        group_size=None, version=1, pretrained=False, **kwargs):
+        group_size=None, version=1, pretrained=False, **kwargs
+):
     """Creates an EfficientNet model.
 
     Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py
@@ -1069,9 +1094,7 @@ def _gen_mixnet_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrai
     return model
 
 
-def _gen_tinynet(
-    variant, model_width=1.0, depth_multiplier=1.0, pretrained=False, **kwargs
-):
+def _gen_tinynet(variant, model_width=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
     """Creates a TinyNet model.
     """
     arch_def = [
@@ -1183,8 +1206,7 @@ def _arch_def(chs: List[int], group_size: int):
     return model
 
 
-def _gen_test_efficientnet(
-        variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
+def _gen_test_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
     """ Minimal test EfficientNet generator.
     """
     arch_def = [

timm/models/mobilenetv3.py

@@ -412,7 +412,9 @@ def _create_mnv3(variant: str, pretrained: bool = False, **kwargs) -> MobileNetV
     return model
 
 
-def _gen_mobilenet_v3_rw(variant: str, channel_multiplier: float = 1.0, pretrained: bool = False, **kwargs) -> MobileNetV3:
+def _gen_mobilenet_v3_rw(
+        variant: str, channel_multiplier: float = 1.0, pretrained: bool = False, **kwargs
+) -> MobileNetV3:
     """Creates a MobileNet-V3 model.
 
     Ref impl: ?
@@ -450,7 +452,9 @@ def _gen_mobilenet_v3_rw(variant: str, channel_multiplier: float = 1.0, pretrain
     return model
 
 
-def _gen_mobilenet_v3(variant: str, channel_multiplier: float = 1.0, pretrained: bool = False, **kwargs) -> MobileNetV3:
+def _gen_mobilenet_v3(
+        variant: str, channel_multiplier: float = 1.0, group_size=None, pretrained: bool = False, **kwargs
+) -> MobileNetV3:
     """Creates a MobileNet-V3 model.
 
     Ref impl: ?
@@ -533,7 +537,7 @@ def _gen_mobilenet_v3(variant: str, channel_multiplier: float = 1.0, pretrained:
             ]
     se_layer = partial(SqueezeExcite, gate_layer='hard_sigmoid', force_act_layer=nn.ReLU, rd_round_fn=round_channels)
     model_kwargs = dict(
-        block_args=decode_arch_def(arch_def),
+        block_args=decode_arch_def(arch_def, group_size=group_size),
         num_features=num_features,
         stem_size=16,
         fix_stem=channel_multiplier < 0.75,
@@ -646,7 +650,9 @@ def _gen_lcnet(variant: str, channel_multiplier: float = 1.0, pretrained: bool =
     return model
 
 
-def _gen_mobilenet_v4(variant: str, channel_multiplier: float = 1.0, pretrained: bool = False, **kwargs) -> MobileNetV3:
+def _gen_mobilenet_v4(
+        variant: str, channel_multiplier: float = 1.0, group_size=None, pretrained: bool = False, **kwargs,
+) -> MobileNetV3:
     """Creates a MobileNet-V4 model.
 
     Ref impl: ?
@@ -877,7 +883,7 @@ def _gen_mobilenet_v4(variant: str, channel_multiplier: float = 1.0, pretrained:
             assert False, f'Unknown variant {variant}.'
 
     model_kwargs = dict(
-        block_args=decode_arch_def(arch_def),
+        block_args=decode_arch_def(arch_def, group_size=group_size),
         head_bias=False,
         head_norm=True,
         num_features=num_features,