Make RandomVerticalFlip torchscriptable

test/test_transforms_tensor.py

@@ -18,21 +18,27 @@ 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_random_horizontal_flip(self):
+    def _test_flip(self, func, method):
         tensor, pil_img = self._create_data()
-        flip_tensor = F.hflip(tensor)
-        flip_pil_img = F.hflip(pil_img)
+        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(F.hflip)
+        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 = T.RandomHorizontalFlip()
+        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

@@ -537,19 +537,22 @@ 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

@@ -28,3 +28,19 @@ def hflip(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/transforms.py

@@ -530,25 +530,29 @@ def __repr__(self):
         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