Vision: Transformations in GPU

[edgarriba]

Is there any plan to support image transformations for GPU?
Doing big transformations e.g resizing (224x224) <-> (64x64) with PIL seems a bit slow.

[soumith]

@edgarriba we are thinking about it. It seems like a good idea, and NVIDIA has the relevant GPU kernels already implemented.

[edgarriba]

nice! that good could be a very appreciate feature :wink:

[edgarriba]

also OpenCV seems a bit faster with this, however I think that such GPU routines are not available in Python

[Maratyszcza]

@edgarriba You may try pytorch/accimage with PR #15 to leverage Intel IPP for preprocessing. Keep in mind that this package is beta (like in beta than nothing) and barely tested.

[edgarriba]

@Maratyszcza thx to point me to this, looks pretty good. I'll try to run and give feedback

[dssa56]

I'd be happy to give this a go, if there's still interest?

[alykhantejani]

I think there might still be interest in having this cc @soumith who might have a stronger opinion here

[radenmuaz]

so any plans for GPU data-augmentations or do we still stick to cpu PIL?

[edgarriba]

@radenmuaz in kornia.org we recently introduced an API for that. Not only supports GPU but also differentiablility. Please check the kornia.augmentation module.

[qhaas]

Those of us on non-x86-64 systems would benefit from GPU accelerated transformations given the lack of optimizations for our architecture at times. For example, we are seeing significant performance disparities between x86-64 and power9 systems that use the same GPU and profiling points the finger at the Power9 CPU taking longer to crunch torchvision/transforms and PIL.

[fmassa]

@qhaas https://github.com/pytorch/vision/pull/2278 is a first step towards making the transforms run seamlessly on the CPU / GPU via torch tensors. For now it will be on a per-image basis so GPUs will probably be slow, but in the future with NestedTensor support we might be able to make it efficient for batches of different image sizes