Allow for arbitrary sequences in torchvision/transforms/functional

[Parskatt]

🚀 The feature

Replace all checks for (list,tuple) in functional with Iterable.

Motivation, pitch

There are about 8 checks in functional right now which requires the argument to be either list or tuple, e.g.
https://github.com/pytorch/vision/blob/main/torchvision/transforms/functional.py#L1059
Instead of performing these checks, simply checking for Iterable allows to use e.g. numpy arrays.

In my usecase i generate parameters using numpy, and I don't want to convert them.

The documentation indicates that general sequences should work, so the current checks are counter-intuitive.

Alternatives

No response

Additional context

No response

cc @vfdev-5 @datumbox

[datumbox]

Sounds reasonable but note we are limited due to JIT compatibility. We wound't be able to add for example numpy arrays in the check because that won't be JIT-scritable. We need to review what exactly is supported by JIT and what's the common denominator/type for iterable objects.

@pmeier @vfdev-5 Good feedback for the new API. Perhaps we could push all these checks on the non-JIT-scriptable high-level kernels and allow the low-level ones just work on input with minimal validation. Thoughts?

[pmeier]

Given that we do not plan to make the transforms (objects not kernels) JIT scriptable, I think we should be able to fix the annotations on the transforms.