Unify Tensor and PIL transforms

Now that most of the tensor transforms have been implemented https://github.com/pytorch/vision/issues/1375, it is time to make unify the implementations for PIL and Tensor so that torchvision.transforms and torchvision.transforms.functional works seamlessly between both datatypes.

As such, we would like that the following work with the same interface:

# functional interface
torchvision.transforms.functional.hflip(pil_image)
torchvision.transforms.functional.hflip(tensor_image)

# class interface
transform = torchvision.transforms.RandomHorizontalFlip(0.3)
transform(pil_image)
transform(tensor_image)

# torchscript support for tensor_image
script_transform = torch.jit.script(transform)
script_transform(tensor_image)

Example PRs adding support for hflip and vflip: https://github.com/pytorch/vision/pull/2282 https://github.com/pytorch/vision/pull/2283

We would like to add support for torchscript to the torchvision.transforms interface as well, which for simplicity might require a few changes. Most notably we would need the transforms to inherit from nn.Module.

Here is a list of transforms that can be readily converted:

• adjust_brightness
• adjust_contrast
• adjust_saturation
• crop
• center_crop
• five_crop
• ten_crop
• pad (#2373 #2345)
• resize (#2394)
• resized_crop (#2396)
• perspective (#2558, #2561)
• adjust_hue (#2566)
• adjust_gamma (#2459)
• rotate (#2495 #2496 #2553)
• affine (#2444, #2468, #2478, #2553)
• to_grayscale

[voldemortX]

@voldemortX thanks for the feedback !

Wondering whether future commits will support fill color other than 0 for tensor affine transformations? It is kind of important for tasks like semantic segmentation when transforming labels.

Could you please open an issue for that such we could track this feature request and see what could be possible to do. Currently, the blocker is with pytorch grid_sample which has

padding_mode=“zeros”: use 0 for out-of-bound grid locations

New issue opened #2887

[vfdev-5]

The issue can be closed as all mentioned transformations now support tensor and pil image as input.