Always pass transform and target_transform to abstract dataset (#1126)

* fixed call to the VisionDataset constructor

* change call from keyword arguments to positional

* changed order of arguments

* removed transforms argument once again

* Fixed call to constructor of parent class

* fixed LSUN

* fixed Caltech256

torchvision/datasets/caltech.py

@@ -26,17 +26,16 @@ class Caltech101(VisionDataset):
             downloaded again.
     """
 
-    def __init__(self, root, target_type="category",
-                 transform=None, target_transform=None,
-                 download=False):
-        super(Caltech101, self).__init__(os.path.join(root, 'caltech101'))
+    def __init__(self, root, target_type="category", transform=None,
+                 target_transform=None, download=False):
+        super(Caltech101, self).__init__(os.path.join(root, 'caltech101'),
+                                         transform=transform,
+                                         target_transform=target_transform)
         makedir_exist_ok(self.root)
         if isinstance(target_type, list):
             self.target_type = target_type
         else:
             self.target_type = [target_type]
-        self.transform = transform
-        self.target_transform = target_transform
 
         if download:
             self.download()
@@ -143,13 +142,11 @@ class Caltech256(VisionDataset):
             downloaded again.
     """
 
-    def __init__(self, root,
-                 transform=None, target_transform=None,
-                 download=False):
-        super(Caltech256, self).__init__(os.path.join(root, 'caltech256'))
+    def __init__(self, root, transform=None, target_transform=None, download=False):
+        super(Caltech256, self).__init__(os.path.join(root, 'caltech256'),
+                                         transform=transform,
+                                         target_transform=target_transform)
         makedir_exist_ok(self.root)
-        self.transform = transform
-        self.target_transform = target_transform
 
         if download:
             self.download()

torchvision/datasets/celeba.py

@@ -48,20 +48,16 @@ class CelebA(VisionDataset):
         ("0B7EVK8r0v71pY0NSMzRuSXJEVkk", "d32c9cbf5e040fd4025c592c306e6668", "list_eval_partition.txt"),
     ]
 
-    def __init__(self, root,
-                 split="train",
-                 target_type="attr",
-                 transform=None, target_transform=None,
-                 download=False):
+    def __init__(self, root, split="train", target_type="attr", transform=None,
+                 target_transform=None, download=False):
         import pandas
-        super(CelebA, self).__init__(root)
+        super(CelebA, self).__init__(root, transform=transform,
+                                     target_transform=target_transform)
         self.split = split
         if isinstance(target_type, list):
             self.target_type = target_type
         else:
             self.target_type = [target_type]
-        self.transform = transform
-        self.target_transform = target_transform
 
         if download:
             self.download()
@@ -70,9 +66,6 @@ class CelebA(VisionDataset):
             raise RuntimeError('Dataset not found or corrupted.' +
                                ' You can use download=True to download it')
 
-        self.transform = transform
-        self.target_transform = target_transform
-
         if split.lower() == "train":
             split = 0
         elif split.lower() == "valid":

torchvision/datasets/cifar.py

@@ -52,13 +52,11 @@ class CIFAR10(VisionDataset):
         'md5': '5ff9c542aee3614f3951f8cda6e48888',
     }
 
-    def __init__(self, root, train=True,
-                 transform=None, target_transform=None,
+    def __init__(self, root, train=True, transform=None, target_transform=None,
                  download=False):
 
-        super(CIFAR10, self).__init__(root)
-        self.transform = transform
-        self.target_transform = target_transform
+        super(CIFAR10, self).__init__(root, transform=transform,
+                                      target_transform=target_transform)
 
         self.train = train  # training set or test set
 

torchvision/datasets/flickr.py

@@ -63,9 +63,8 @@ class Flickr8k(VisionDataset):
     """
 
     def __init__(self, root, ann_file, transform=None, target_transform=None):
-        super(Flickr8k, self).__init__(root)
-        self.transform = transform
-        self.target_transform = target_transform
+        super(Flickr8k, self).__init__(root, transform=transform,
+                                       target_transform=target_transform)
         self.ann_file = os.path.expanduser(ann_file)
 
         # Read annotations and store in a dict
@@ -115,9 +114,8 @@ class Flickr30k(VisionDataset):
     """
 
     def __init__(self, root, ann_file, transform=None, target_transform=None):
-        super(Flickr30k, self).__init__(root)
-        self.transform = transform
-        self.target_transform = target_transform
+        super(Flickr30k, self).__init__(root, transform=transform,
+                                        target_transform=target_transform)
         self.ann_file = os.path.expanduser(ann_file)
 
         # Read annotations and store in a dict

torchvision/datasets/folder.py

@@ -86,10 +86,10 @@ class DatasetFolder(VisionDataset):
         targets (list): The class_index value for each image in the dataset
     """
 
-    def __init__(self, root, loader, extensions=None, transform=None, target_transform=None, is_valid_file=None):
-        super(DatasetFolder, self).__init__(root)
-        self.transform = transform
-        self.target_transform = target_transform
+    def __init__(self, root, loader, extensions=None, transform=None,
+                 target_transform=None, is_valid_file=None):
+        super(DatasetFolder, self).__init__(root, transform=transform,
+                                            target_transform=target_transform)
         classes, class_to_idx = self._find_classes(self.root)
         samples = make_dataset(self.root, class_to_idx, extensions, is_valid_file)
         if len(samples) == 0:

torchvision/datasets/lsun.py

@@ -15,9 +15,8 @@ else:
 class LSUNClass(VisionDataset):
     def __init__(self, root, transform=None, target_transform=None):
         import lmdb
-        super(LSUNClass, self).__init__(root)
-        self.transform = transform
-        self.target_transform = target_transform
+        super(LSUNClass, self).__init__(root, transform=transform,
+                                        target_transform=target_transform)
 
         self.env = lmdb.open(root, max_readers=1, readonly=True, lock=False,
                              readahead=False, meminit=False)
@@ -68,11 +67,9 @@ class LSUN(VisionDataset):
             target and transforms it.
     """
 
-    def __init__(self, root, classes='train',
-                 transform=None, target_transform=None):
-        super(LSUN, self).__init__(root)
-        self.transform = transform
-        self.target_transform = target_transform
+    def __init__(self, root, classes='train', transform=None, target_transform=None):
+        super(LSUN, self).__init__(root, transform=transform,
+                                   target_transform=target_transform)
         categories = ['bedroom', 'bridge', 'church_outdoor', 'classroom',
                       'conference_room', 'dining_room', 'kitchen',
                       'living_room', 'restaurant', 'tower']

torchvision/datasets/mnist.py

@@ -57,10 +57,10 @@ class MNIST(VisionDataset):
         warnings.warn("test_data has been renamed data")
         return self.data
 
-    def __init__(self, root, train=True, transform=None, target_transform=None, download=False):
-        super(MNIST, self).__init__(root)
-        self.transform = transform
-        self.target_transform = target_transform
+    def __init__(self, root, train=True, transform=None, target_transform=None,
+                 download=False):
+        super(MNIST, self).__init__(root, transform=transform,
+                                    target_transform=target_transform)
         self.train = train  # training set or test set
 
         if download:

torchvision/datasets/omniglot.py

@@ -28,12 +28,10 @@ class Omniglot(VisionDataset):
         'images_evaluation': '6b91aef0f799c5bb55b94e3f2daec811'
     }
 
-    def __init__(self, root, background=True,
-                 transform=None, target_transform=None,
+    def __init__(self, root, background=True, transform=None, target_transform=None,
                  download=False):
-        super(Omniglot, self).__init__(join(root, self.folder))
-        self.transform = transform
-        self.target_transform = target_transform
+        super(Omniglot, self).__init__(join(root, self.folder), transform=transform,
+                                       target_transform=target_transform)
         self.background = background
 
         if download:

torchvision/datasets/phototour.py

@@ -65,8 +65,7 @@ class PhotoTour(VisionDataset):
     matches_files = 'm50_100000_100000_0.txt'
 
     def __init__(self, root, name, train=True, transform=None, download=False):
-        super(PhotoTour, self).__init__(root)
-        self.transform = transform
+        super(PhotoTour, self).__init__(root, transform=transform)
         self.name = name
         self.data_dir = os.path.join(self.root, name)
         self.data_down = os.path.join(self.root, '{}.zip'.format(name))

torchvision/datasets/sbu.py

@@ -24,11 +24,9 @@ class SBU(VisionDataset):
     filename = "SBUCaptionedPhotoDataset.tar.gz"
     md5_checksum = '9aec147b3488753cf758b4d493422285'
 
-    def __init__(self, root, transform=None, target_transform=None,
-                 download=True):
-        super(SBU, self).__init__(root)
-        self.transform = transform
-        self.target_transform = target_transform
+    def __init__(self, root, transform=None, target_transform=None, download=True):
+        super(SBU, self).__init__(root, transform=transform,
+                                  target_transform=target_transform)
 
         if download:
             self.download()

torchvision/datasets/semeion.py

@@ -24,11 +24,9 @@ class SEMEION(VisionDataset):
     filename = "semeion.data"
     md5_checksum = 'cb545d371d2ce14ec121470795a77432'
 
-    def __init__(self, root, transform=None, target_transform=None,
-                 download=True):
-        super(SEMEION, self).__init__(root)
-        self.transform = transform
-        self.target_transform = target_transform
+    def __init__(self, root, transform=None, target_transform=None, download=True):
+        super(SEMEION, self).__init__(root, transform=transform,
+                                      target_transform=target_transform)
 
         if download:
             self.download()

torchvision/datasets/stl10.py

@@ -46,15 +46,14 @@ class STL10(VisionDataset):
     ]
     splits = ('train', 'train+unlabeled', 'unlabeled', 'test')
 
-    def __init__(self, root, split='train', folds=None,
-                 transform=None, target_transform=None, download=False):
+    def __init__(self, root, split='train', folds=None, transform=None,
+                 target_transform=None, download=False):
         if split not in self.splits:
             raise ValueError('Split "{}" not found. Valid splits are: {}'.format(
                 split, ', '.join(self.splits),
             ))
-        super(STL10, self).__init__(root)
-        self.transform = transform
-        self.target_transform = target_transform
+        super(STL10, self).__init__(root, transform=transform,
+                                    target_transform=target_transform)
         self.split = split  # train/test/unlabeled set
         self.folds = folds  # one of the 10 pre-defined folds or the full dataset
 

torchvision/datasets/svhn.py

@@ -39,11 +39,10 @@ class SVHN(VisionDataset):
         'extra': ["http://ufldl.stanford.edu/housenumbers/extra_32x32.mat",
                   "extra_32x32.mat", "a93ce644f1a588dc4d68dda5feec44a7"]}
 
-    def __init__(self, root, split='train',
-                 transform=None, target_transform=None, download=False):
-        super(SVHN, self).__init__(root)
-        self.transform = transform
-        self.target_transform = target_transform
+    def __init__(self, root, split='train', transform=None, target_transform=None,
+                 download=False):
+        super(SVHN, self).__init__(root, transform=transform,
+                                   target_transform=target_transform)
         self.split = split  # training set or test set or extra set
 
         if self.split not in self.split_list:

torchvision/datasets/usps.py

@@ -37,8 +37,10 @@ class USPS(VisionDataset):
         ],
     }
 
-    def __init__(self, root, train=True, transform=None, target_transform=None, download=False):
-        super(USPS, self).__init__(root, transform=transform, target_transform=target_transform)
+    def __init__(self, root, train=True, transform=None, target_transform=None,
+                 download=False):
+        super(USPS, self).__init__(root, transform=transform,
+                                   target_transform=target_transform)
         split = 'train' if train else 'test'
         url, filename, checksum = self.split_list[split]
         full_path = os.path.join(self.root, filename)