Make RandomHorizontalFlip torchscriptable (#2282)

* Make RandomHorizontalFlip torchscriptable

* Make _is_tensor_a_torch_image more generic

* Make RandomVerticalFlip torchscriptable (#2283)

* Make RandomVerticalFlip torchscriptable

* Fix lint

test/test_transforms_tensor.py

+import torch
+from torchvision import transforms as T
+from torchvision.transforms import functional as F
+from PIL import Image
+
+import numpy as np
+
+import unittest
+
+
+class Tester(unittest.TestCase):
+    def _create_data(self, height=3, width=3, channels=3):
+        tensor = torch.randint(0, 255, (channels, height, width), dtype=torch.uint8)
+        pil_img = Image.fromarray(tensor.permute(1, 2, 0).contiguous().numpy())
+        return tensor, pil_img
+
+    def compareTensorToPIL(self, tensor, pil_image):
+        pil_tensor = torch.as_tensor(np.array(pil_image).transpose((2, 0, 1)))
+        self.assertTrue(tensor.equal(pil_tensor))
+
+    def _test_flip(self, func, method):
+        tensor, pil_img = self._create_data()
+        flip_tensor = getattr(F, func)(tensor)
+        flip_pil_img = getattr(F, func)(pil_img)
+        self.compareTensorToPIL(flip_tensor, flip_pil_img)
+
+        scripted_fn = torch.jit.script(getattr(F, func))
+        flip_tensor_script = scripted_fn(tensor)
+        self.assertTrue(flip_tensor.equal(flip_tensor_script))
+
+        # test for class interface
+        f = getattr(T, method)()
+        scripted_fn = torch.jit.script(f)
+        scripted_fn(tensor)
+
+    def test_random_horizontal_flip(self):
+        self._test_flip('hflip', 'RandomHorizontalFlip')
+
+    def test_random_vertical_flip(self):
+        self._test_flip('vflip', 'RandomVerticalFlip')
+
+
+if __name__ == '__main__':
+    unittest.main()

torchvision/transforms/functional.py

 import torch
+from torch import Tensor
 import math
 from PIL import Image, ImageOps, ImageEnhance, __version__ as PILLOW_VERSION
 try:
@@ -11,6 +12,9 @@ import numbers
 from collections.abc import Sequence, Iterable
 import warnings
 
+from . import functional_pil as F_pil
+from . import functional_tensor as F_t
+
 
 def _is_pil_image(img):
     if accimage is not None:
@@ -434,19 +438,22 @@ def resized_crop(img, top, left, height, width, size, interpolation=Image.BILINE
     return img
 
 
-def hflip(img):
-    """Horizontally flip the given PIL Image.
+def hflip(img: Tensor) -> Tensor:
+    """Horizontally flip the given PIL Image or torch Tensor.
 
     Args:
-        img (PIL Image): Image to be flipped.
+        img (PIL Image or Torch Tensor): Image to be flipped. If img
+            is a Tensor, it is expected to be in [..., H, W] format,
+            where ... means it can have an arbitrary number of trailing
+            dimensions.
 
     Returns:
         PIL Image:  Horizontally flipped image.
     """
-    if not _is_pil_image(img):
-        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+    if not isinstance(img, torch.Tensor):
+        return F_pil.hflip(img)
 
-    return img.transpose(Image.FLIP_LEFT_RIGHT)
+    return F_t.hflip(img)
 
 
 def _parse_fill(fill, img, min_pil_version):
@@ -536,19 +543,22 @@ def perspective(img, startpoints, endpoints, interpolation=Image.BICUBIC, fill=N
     return img.transform(img.size, Image.PERSPECTIVE, coeffs, interpolation, **opts)
 
 
-def vflip(img):
-    """Vertically flip the given PIL Image.
+def vflip(img: Tensor) -> Tensor:
+    """Vertically flip the given PIL Image or torch Tensor.
 
     Args:
-        img (PIL Image): Image to be flipped.
+        img (PIL Image or Torch Tensor): Image to be flipped. If img
+            is a Tensor, it is expected to be in [..., H, W] format,
+            where ... means it can have an arbitrary number of trailing
+            dimensions.
 
     Returns:
         PIL Image:  Vertically flipped image.
     """
-    if not _is_pil_image(img):
-        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+    if not isinstance(img, torch.Tensor):
+        return F_pil.vflip(img)
 
-    return img.transpose(Image.FLIP_TOP_BOTTOM)
+    return F_t.vflip(img)
 
 
 def five_crop(img, size):

torchvision/transforms/functional_pil.py

+import torch
+try:
+    import accimage
+except ImportError:
+    accimage = None
+from PIL import Image, ImageOps, ImageEnhance, __version__ as PILLOW_VERSION
+
+
+@torch.jit.unused
+def _is_pil_image(img):
+    if accimage is not None:
+        return isinstance(img, (Image.Image, accimage.Image))
+    else:
+        return isinstance(img, Image.Image)
+
+
+@torch.jit.unused
+def hflip(img):
+    """Horizontally flip the given PIL Image.
+
+    Args:
+        img (PIL Image): Image to be flipped.
+
+    Returns:
+        PIL Image:  Horizontally flipped image.
+    """
+    if not _is_pil_image(img):
+        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+
+    return img.transpose(Image.FLIP_LEFT_RIGHT)
+
+
+@torch.jit.unused
+def vflip(img):
+    """Vertically flip the given PIL Image.
+
+    Args:
+        img (PIL Image): Image to be flipped.
+
+    Returns:
+        PIL Image:  Vertically flipped image.
+    """
+    if not _is_pil_image(img):
+        raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
+
+    return img.transpose(Image.FLIP_TOP_BOTTOM)

torchvision/transforms/functional_tensor.py

 import torch
-import torchvision.transforms.functional as F
 from torch import Tensor
 from torch.jit.annotations import Optional, List, BroadcastingList2, Tuple
 
 
 def _is_tensor_a_torch_image(input):
-    return len(input.shape) == 3
+    return input.ndim >= 2
 
 
 def vflip(img):

torchvision/transforms/transforms.py

@@ -500,25 +500,29 @@ class RandomCrop(object):
         return self.__class__.__name__ + '(size={0}, padding={1})'.format(self.size, self.padding)
 
 
-class RandomHorizontalFlip(object):
-    """Horizontally flip the given PIL Image randomly with a given probability.
+class RandomHorizontalFlip(torch.nn.Module):
+    """Horizontally flip the given image randomly with a given probability.
+    The image can be a PIL Image or a torch Tensor, in which case it is expected
+    to have [..., H, W] shape, where ... means an arbitrary number of leading
+    dimensions
 
     Args:
         p (float): probability of the image being flipped. Default value is 0.5
     """
 
     def __init__(self, p=0.5):
+        super().__init__()
         self.p = p
 
-    def __call__(self, img):
+    def forward(self, img):
         """
         Args:
-            img (PIL Image): Image to be flipped.
+            img (PIL Image or Tensor): Image to be flipped.
 
         Returns:
-            PIL Image: Randomly flipped image.
+            PIL Image or Tensor: Randomly flipped image.
         """
-        if random.random() < self.p:
+        if torch.rand(1) < self.p:
             return F.hflip(img)
         return img
 
@@ -526,25 +530,29 @@ class RandomHorizontalFlip(object):
         return self.__class__.__name__ + '(p={})'.format(self.p)
 
 
-class RandomVerticalFlip(object):
+class RandomVerticalFlip(torch.nn.Module):
     """Vertically flip the given PIL Image randomly with a given probability.
+    The image can be a PIL Image or a torch Tensor, in which case it is expected
+    to have [..., H, W] shape, where ... means an arbitrary number of leading
+    dimensions
 
     Args:
         p (float): probability of the image being flipped. Default value is 0.5
     """
 
     def __init__(self, p=0.5):
+        super().__init__()
         self.p = p
 
-    def __call__(self, img):
+    def forward(self, img):
         """
         Args:
-            img (PIL Image): Image to be flipped.
+            img (PIL Image or Tensor): Image to be flipped.
 
         Returns:
-            PIL Image: Randomly flipped image.
+            PIL Image or Tensor: Randomly flipped image.
         """
-        if random.random() < self.p:
+        if torch.rand(1) < self.p:
             return F.vflip(img)
         return img