Revert "Adding a DatasetFolder class."

docs/source/datasets.rst

@@ -4,11 +4,11 @@ torchvision.datasets
 All datasets are subclasses of :class:`torch.utils.data.Dataset`
 i.e, they have ``__getitem__`` and ``__len__`` methods implemented.
 Hence, they can all be passed to a :class:`torch.utils.data.DataLoader`
-which can load multiple samples parallelly using ``torch.multiprocessing`` workers.
+which can load multiple samples parallelly using ``torch.multiprocessing`` workers. 
 For example: ::
-
+    
     imagenet_data = torchvision.datasets.ImageFolder('path/to/imagenet_root/')
-    data_loader = torch.utils.data.DataLoader(imagenet_data,
+    data_loader = torch.utils.data.DataLoader(imagenet_data, 
                                               batch_size=4,
                                               shuffle=True,
                                               num_workers=args.nThreads)
@@ -22,7 +22,7 @@ All the datasets have almost similar API. They all have two common arguments:
 ``transform`` and  ``target_transform`` to transform the input and target respectively.
 
 
-.. currentmodule:: torchvision.datasets
+.. currentmodule:: torchvision.datasets 
 
 
 MNIST
@@ -78,14 +78,6 @@ ImageFolder
   :members: __getitem__
   :special-members:
 
-DatasetFolder
-~~~~~~~~~~~~~
-
-.. autoclass:: DatasetFolder
-  :members: __getitem__
-  :special-members:
-
-
 
 Imagenet-12
 ~~~~~~~~~~~
@@ -129,3 +121,4 @@ PhotoTour
 .. autoclass:: PhotoTour
   :members: __getitem__
   :special-members:
+

setup.py

@@ -33,7 +33,6 @@ def find_version(*file_paths):
     'pillow >= 4.1.1',
     'six',
     'torch',
-    'mock',
 ]
 
 setup(

torchvision/datasets/__init__.py

@@ -1,5 +1,5 @@
 from .lsun import LSUN, LSUNClass
-from .folder import ImageFolder, DatasetFolder
+from .folder import ImageFolder
 from .coco import CocoCaptions, CocoDetection
 from .cifar import CIFAR10, CIFAR100
 from .stl10 import STL10
@@ -11,7 +11,7 @@
 from .omniglot import Omniglot
 
 __all__ = ('LSUN', 'LSUNClass',
-           'ImageFolder', 'DatasetFolder', 'FakeData',
+           'ImageFolder', 'FakeData',
            'CocoCaptions', 'CocoDetection',
            'CIFAR10', 'CIFAR100', 'EMNIST', 'FashionMNIST',
            'MNIST', 'STL10', 'SVHN', 'PhotoTour', 'SEMEION',

torchvision/datasets/folder.py

@@ -1,13 +1,14 @@
 import torch.utils.data as data
 
 from PIL import Image
-
 import os
 import os.path
 
+IMG_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm']
+
 
-def has_file_allowed_extension(filename, extensions):
-    """Checks if a file is an allowed extension.
+def is_image_file(filename):
+    """Checks if a file is an image.
 
     Args:
         filename (string): path to a file
@@ -16,7 +17,7 @@ def has_file_allowed_extension(filename, extensions):
         bool: True if the filename ends with a known image extension
     """
     filename_lower = filename.lower()
-    return any(filename_lower.endswith(ext) for ext in extensions)
+    return any(filename_lower.endswith(ext) for ext in IMG_EXTENSIONS)
 
 
 def find_classes(dir):
@@ -26,7 +27,7 @@ def find_classes(dir):
     return classes, class_to_idx
 
 
-def make_dataset(dir, class_to_idx, extensions):
+def make_dataset(dir, class_to_idx):
     images = []
     dir = os.path.expanduser(dir)
     for target in sorted(os.listdir(dir)):
@@ -36,93 +37,14 @@ def make_dataset(dir, class_to_idx, extensions):
 
         for root, _, fnames in sorted(os.walk(d)):
             for fname in sorted(fnames):
-                if has_file_allowed_extension(fname, extensions):
+                if is_image_file(fname):
                     path = os.path.join(root, fname)
                     item = (path, class_to_idx[target])
                     images.append(item)
 
     return images
 
 
-class DatasetFolder(data.Dataset):
-    """A generic data loader where the samples are arranged in this way: ::
-
-        root/class_x/xxx.ext
-        root/class_x/xxy.ext
-        root/class_x/xxz.ext
-
-        root/class_y/123.ext
-        root/class_y/nsdf3.ext
-        root/class_y/asd932_.ext
-
-    Args:
-        root (string): Root directory path.
-        loader (callable): A function to load a sample given its path.
-        extensions (list[string]): A list of allowed extensions.
-        transform (callable, optional): A function/transform that takes in
-            a sample and returns a transformed version.
-            E.g, ``transforms.RandomCrop`` for images.
-        target_transform (callable, optional): A function/transform that takes
-            in the target and transforms it.
-
-     Attributes:
-        classes (list): List of the class names.
-        class_to_idx (dict): Dict with items (class_name, class_index).
-        samples (list): List of (sample path, class_index) tuples
-    """
-
-    def __init__(self, root, loader, extensions, transform=None, target_transform=None):
-        classes, class_to_idx = find_classes(root)
-        samples = make_dataset(root, class_to_idx, extensions)
-        if len(samples) == 0:
-            raise(RuntimeError("Found 0 files in subfolders of: " + root + "\n"
-                               "Supported extensions are: " + ",".join(extensions)))
-
-        self.root = root
-        self.loader = loader
-        self.extensions = extensions
-
-        self.classes = classes
-        self.class_to_idx = class_to_idx
-        self.samples = samples
-
-        self.transform = transform
-        self.target_transform = target_transform
-
-    def __getitem__(self, index):
-        """
-        Args:
-            index (int): Index
-
-        Returns:
-            tuple: (sample, target) where target is class_index of the target class.
-        """
-        path, target = self.samples[index]
-        sample = self.loader(path)
-        if self.transform is not None:
-            sample = self.transform(sample)
-        if self.target_transform is not None:
-            target = self.target_transform(target)
-
-        return sample, target
-
-    def __len__(self):
-        return len(self.samples)
-
-    def __repr__(self):
-        fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
-        fmt_str += '    Number of datapoints: {}\n'.format(self.__len__())
-        fmt_str += '    Root Location: {}\n'.format(self.root)
-        tmp = '    Transforms (if any): '
-        fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
-        tmp = '    Target Transforms (if any): '
-        fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
-        return fmt_str
-
-
-IMG_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm']
-
-
 def pil_loader(path):
     # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
     with open(path, 'rb') as f:
@@ -147,7 +69,7 @@ def default_loader(path):
         return pil_loader(path)
 
 
-class ImageFolder(DatasetFolder):
+class ImageFolder(data.Dataset):
     """A generic data loader where the images are arranged in this way: ::
 
         root/dog/xxx.png
@@ -171,9 +93,49 @@ class ImageFolder(DatasetFolder):
         class_to_idx (dict): Dict with items (class_name, class_index).
         imgs (list): List of (image path, class_index) tuples
     """
+
     def __init__(self, root, transform=None, target_transform=None,
                  loader=default_loader):
-        super(ImageFolder, self).__init__(root, loader, IMG_EXTENSIONS,
-                                          transform=transform,
-                                          target_transform=target_transform)
-        self.imgs = self.samples
+        classes, class_to_idx = find_classes(root)
+        imgs = make_dataset(root, class_to_idx)
+        if len(imgs) == 0:
+            raise(RuntimeError("Found 0 images in subfolders of: " + root + "\n"
+                               "Supported image extensions are: " + ",".join(IMG_EXTENSIONS)))
+
+        self.root = root
+        self.imgs = imgs
+        self.classes = classes
+        self.class_to_idx = class_to_idx
+        self.transform = transform
+        self.target_transform = target_transform
+        self.loader = loader
+
+    def __getitem__(self, index):
+        """
+        Args:
+            index (int): Index
+
+        Returns:
+            tuple: (image, target) where target is class_index of the target class.
+        """
+        path, target = self.imgs[index]
+        img = self.loader(path)
+        if self.transform is not None:
+            img = self.transform(img)
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return img, target
+
+    def __len__(self):
+        return len(self.imgs)
+
+    def __repr__(self):
+        fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
+        fmt_str += '    Number of datapoints: {}\n'.format(self.__len__())
+        fmt_str += '    Root Location: {}\n'.format(self.root)
+        tmp = '    Transforms (if any): '
+        fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
+        tmp = '    Target Transforms (if any): '
+        fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
+        return fmt_str