Add uint8 support for interpolate and grid-sample in PyTorch

[fmassa]

🚀 Feature

With the addition of torch tensor support for the transforms (following #1375), there are two operators that will require extra attention in order for them to be as efficient as the PIL implementations.

Indeed, while we can implement resize via torch.nn.functional.interpolate and rotate via torch.nn.functional.grid_sample, those operators for now only support floating-point types, so for now we need to perform a .float() -> interpolate() -> byte() in order to maintain compatibility, which is wasteful.

It would be great if they could be extended to support uint8 (and maybe other integer types) as well.

A first PR adding support for uint8 to nearest mode interpolate has been sent in pytorch/pytorch#35029

Something to keep in mind: the interpolate function is under optimization in pytorch/pytorch#34864, so this should be kept in mind to avoid conflicts.

[karthikprasad]

Curious to know if this issue is still high priority? I am especially interested in uint8 support for interpolate in bilinear mode.

[zhiqwang]

Hi @karthikprasad , You can see the progression on this #6619 (comment)

[NicolasHug]

Hi @karthikprasad , this is planned work, but without a real ETA yet. If this is useful to you, we may be able to bump the priority. I'll reach out internally

[NicolasHug]

I started working on this in pytorch/pytorch#87863