[prototype] Fix BC-breakages on input params of F

[datumbox]

Depends on #6635

A number of dispatchers on stable F define one typing but handle multiple. This comes from the days where JIT didn't support Union. One such an example, is the size parameter of resize which is declared as List[int] but int is also handled internally. Due to the use of these fake types, the prototype F doesn't currently have those mitigations. This PR, tries to restore BC-breakages by correcting the types on prototype F and handling all possible inputs. The issue was first reported by @vfdev-5 for resize. I took a look and here is some additional dispatchers affected:

• resize: size
• center_crop: output_size
• five_crop: size
• ten_crop: size
• gaussian_blur: kernel_size, sigma
• affine: angle, shear

Here is the proposed changes for each method:

• Change the typing info on the prototype to reflect the true supported types. Do this for _Feature methods, F dispatchers, the actual kernels and the Transform constructors. Edit: Unfortunately this is not possible for the kernels due to JIT. See this [prototype] Fix BC-breakages on input params of F #6636 (review)
• Add the actual code mitigations in the kernels instead on the dispatchers, so that the behaviour remains the same no matter what the user is calling (kernels are to become public in the new API).

[pmeier]

I dislike the nested if here. Maybe

Suggested change

	if isinstance(size, int):
	image_size = [size, size]
	else:
	image_size = (size[0], size[0]) if len(size) == 1 else (size[0], size[1])
	if isinstance(size, int):
	image_size = [size, size]
	elif len(size) == 1:
	image_size = [size[0], size[0]]

?

This is almost equivalent with what we had before with the only difference that the previous solution silently ignored size[2:]. Meaning, it would still work if we pass sizes of arbitrary size. My suggestion will now raise an exception in the kernel.

[datumbox]

I tried it but the bbox expected a tuple and this needs to be handled explicitly to make mypy happy. I'll revert the previous approach which just handles the integer and maintains the previous treatment on the else but I'm happy to review on the nits where and how this should be handled. We need to discuss where exactly the exception should be thrown in this case. Possibly on the new_like method itself?

[pmeier]

Plus, mypy seems to be complaining about the change somewhere.

[datumbox]

@pmeier @vfdev-5 Thanks for the comments. The PR is half-baked so I'll make sure the nits and tests are in order before finalizing. I was hoping you can provide feedback around the overall approach described on the PR description (aka where we place the mitigations, how we align the typing etc). I take it from your comments we are aligned?

[datumbox]

The main changes are mostly done, though there are lots of styles / nits we might want to see.

Several JIT tests are failing but this might be due to #6635 not being merged yet. Perhaps the following error is related to my comment at https://github.com/pytorch/vision/pull/6635/files#r978934554:

test/test_prototype_transforms_functional.py:57 (TestKernels.test_scripted_vs_eager[cpu-resize_image_tensor-167])
Traceback (most recent call last):
  File "test/test_prototype_transforms_functional.py", line 66, in test_scripted_vs_eager
    actual = kernel_scripted(*args, **kwargs)
RuntimeError: resize_image_tensor() Expected a value of type 'Union[List[int], int]' for argument 'size' but instead found type 'tuple'.
Position: 1
Value: (23, 11)
Declaration: resize_image_tensor(Tensor image, Union(int[], int) size, Enum<__torch__.torchvision.transforms.functional.InterpolationMode> interpolation=Enum<InterpolationMode.BILINEAR>, int? max_size=None, bool antialias=False) -> Tensor
Cast error details: Expected a member of Union[List[int], int] but instead found type Tuple[int, int]

[datumbox]

Since the _setup_float_or_seq() will do the job for us, we only need to handle the integer case.

[datumbox]

Moved as-is.

[datumbox]

Unfortunately the moment we put all the real types in the signatures (for example Union[int, List[int]]) the JIT stops working when provided with a Tuple[int, int]. Currently there seems to be some mitigation in JIT that handles Tuple[int, int] even when List[int] is expected. This mitigation breaks the moment the integers are added. It's a shame we cant define the real types but maintaining JIT-scriptability is more important. I'll be changing back the typing modifications, keeping only other BC-fixes in this PR.

Reference:

_______ TestKernels.test_scripted_vs_eager[cpu-resize_image_tensor-273] ________
Traceback (most recent call last):
  File "/home/runner/work/vision/vision/test/test_prototype_transforms_functional.py", line 66, in test_scripted_vs_eager
    actual = kernel_scripted(*args, **kwargs)
RuntimeError: resize_image_tensor() Expected a value of type 'Union[List[int], int]' for argument 'size' but instead found type 'tuple'.
Position: 1
Value: (31,)
Declaration: resize_image_tensor(Tensor image, Union(int[], int) size, Enum<__torch__.torchvision.transforms.functional.InterpolationMode> interpolation=Enum<InterpolationMode.BILINEAR>, int? max_size=None, bool antialias=False) -> Tensor
Cast error details: Expected a member of Union[List[int], int] but instead found type Tuple[int]

[pmeier]

LGTM if CI is green. Thanks Vasilis!