add a fakedata generator for easy debugging

torchvision/datasets/__init__.py

@@ -6,9 +6,10 @@ from .stl10 import STL10
 from .mnist import MNIST
 from .svhn import SVHN
 from .phototour import PhotoTour
+from .fakedata import FakeData
 
 __all__ = ('LSUN', 'LSUNClass',
-           'ImageFolder',
+           'ImageFolder', 'FakeData',
            'CocoCaptions', 'CocoDetection',
            'CIFAR10', 'CIFAR100',
            'MNIST', 'STL10', 'SVHN', 'PhotoTour')

torchvision/datasets/fakedata.py

+import torch
+import torch.utils.data as data
+from .. import transforms
+
+class FakeData(data.Dataset):
+    """A fake dataset that returns randomly generated images and returns them as PIL images
+
+    Args:
+        size (int, optional): Size of the dataset. Default: 1000 images
+        image_size(tuple, optional): Size if the returned images. Default: (3, 224, 224)
+        num_classes(int, optional): Number of classes in the datset. Default: 10
+        transform (callable, optional): A function/transform that  takes in an PIL image
+            and returns a transformed version. E.g, ``transforms.RandomCrop``
+        target_transform (callable, optional): A function/transform that takes in the
+            target and transforms it.
+
+    """
+
+    def __init__(self, size=1000, image_size=(3, 224, 224), num_classes=10, transform=None, target_transform=None):
+        self.size = size
+        self.num_classes = num_classes
+        self.image_size = image_size
+        self.transform = transform
+        self.target_transform = target_transform
+
+    def __getitem__(self, index):
+        """
+        Args:
+            index (int): Index
+
+        Returns:
+            tuple: (image, target) where target is class_index of the target class.
+        """
+        # create random image that is consistent with the index id
+        rng_state = torch.get_rng_state()
+        torch.manual_seed(index)
+        img = torch.randn(*self.image_size)
+        target = torch.Tensor(1).random_(0, self.num_classes)[0]
+        torch.set_rng_state(rng_state)
+
+        # convert to PIL Image
+        img = transforms.ToPILImage()(img)
+        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 self.size