[fbsync] Add warning in docs of Resize about different results for PIL and tensors (#3615)

Summary:
* docs for resize

* address comment: describe antialiasing

Reviewed By: NicolasHug

Differential Revision: D27706945

fbshipit-source-id: e48c865c09f204b75d5e209a7ba45166fc913e18

Author: fmassa

torchvision/transforms/functional.py

@@ -346,6 +346,12 @@ def resize(img: Tensor, size: List[int], interpolation: InterpolationMode = Inte
     If the image is torch Tensor, it is expected
     to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
 
+    .. warning::
+        The output image might be different depending on its type: when downsampling, the interpolation of PIL images
+        and tensors is slightly different, because PIL applies antialiasing. This may lead to significant differences
+        in the performance of a network. Therefore, it is preferable to train and serve a model with the same input
+        types.
+
     Args:
         img (PIL Image or Tensor): Image to be resized.
         size (sequence or int): Desired output size. If size is a sequence like

torchvision/transforms/transforms.py

@@ -229,6 +229,12 @@ class Resize(torch.nn.Module):
     If the image is torch Tensor, it is expected
     to have [..., H, W] shape, where ... means an arbitrary number of leading dimensions
 
+    .. warning::
+        The output image might be different depending on its type: when downsampling, the interpolation of PIL images
+        and tensors is slightly different, because PIL applies antialiasing. This may lead to significant differences
+        in the performance of a network. Therefore, it is preferable to train and serve a model with the same input
+        types.
+
     Args:
         size (sequence or int): Desired output size. If size is a sequence like
             (h, w), output size will be matched to this. If size is an int,