Add new transform-utils for common type transforms and apply to CreateImageDataLoader

Author: dwoiwode

autoPyTorch/pipeline/nodes/image/create_image_dataloader.py

@@ -18,6 +18,9 @@
 from torch.utils.data.sampler import SubsetRandomSampler
 from torchvision import datasets, models, transforms
 
+from autoPyTorch.utils.transforms import transform_int64
+
+
 class CreateImageDataLoader(CreateDataLoader):
 
     def fit(self, pipeline_config, hyperparameter_config, X, Y, train_indices, valid_indices, train_transform, valid_transform, dataset_info):
@@ -27,18 +30,19 @@ def fit(self, pipeline_config, hyperparameter_config, X, Y, train_indices, valid
 
         torch.manual_seed(pipeline_config["random_seed"])
         hyperparameter_config = ConfigWrapper(self.get_name(), hyperparameter_config)
+        to_int64 = transform_int64
 
         if dataset_info.default_dataset:
             train_dataset = dataset_info.default_dataset(root=pipeline_config['default_dataset_download_dir'], train=True, download=True, transform=train_transform)
             if valid_indices is not None:
                 valid_dataset = dataset_info.default_dataset(root=pipeline_config['default_dataset_download_dir'], train=True, download=True, transform=valid_transform)
         elif len(X.shape) > 1:
-            train_dataset = XYDataset(X, Y, transform=train_transform, target_transform=lambda y: y.astype(np.int64))
-            valid_dataset = XYDataset(X, Y, transform=valid_transform, target_transform=lambda y: y.astype(np.int64))
+            train_dataset = XYDataset(X, Y, transform=train_transform, target_transform=to_int64)
+            valid_dataset = XYDataset(X, Y, transform=valid_transform, target_transform=to_int64)
         else:
-            train_dataset = ImageFilelist(X, Y, transform=train_transform, target_transform=lambda y: y.astype(np.int64), cache_size=pipeline_config['dataloader_cache_size_mb'] * 1000, image_size=dataset_info.x_shape[2:])
+            train_dataset = ImageFilelist(X, Y, transform=train_transform, target_transform=to_int64, cache_size=pipeline_config['dataloader_cache_size_mb'] * 1000, image_size=dataset_info.x_shape[2:])
             if valid_indices is not None:
-                valid_dataset = ImageFilelist(X, Y, transform=valid_transform, target_transform=lambda y: y.astype(np.int64), cache_size=0, image_size=dataset_info.x_shape[2:])
+                valid_dataset = ImageFilelist(X, Y, transform=valid_transform, target_transform=to_int64, cache_size=0, image_size=dataset_info.x_shape[2:])
                 valid_dataset.cache = train_dataset.cache
 
         train_loader = DataLoader(

autoPyTorch/utils/transforms.py

@@ -0,0 +1,5 @@
+import numpy as np
+
+
+def transform_int64(y):
+    return y.astype(np.int64)