Normalize CV-CUDA Backend

[justincdavis]

Summary

This PR adds the CV-CUDA backend kernel for the Normalize transform.

How to use

import cvcuda
import torchvision.transforms.v2.functional as F

cvc_tensor = cvcuda.Tensor((1, 224, 224, 3), cvcuda.Type.F32, cvcuda.TensorLayout.NHWC)
# Dispatches to F.normalize_cvcuda
normalized_tensor = F.normalize(cvc_tensor, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])

Run unit tests

pytest test/test_transforms_v2.py::TestNormalizeCVCUDA
...
60 passed in 0.59s

[pytorch-bot]

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[AntoineSimoulin]

Hey @justincdavis, thanks for submitting the PR, this is looking good:) I left some minor changes. I think we mainly need to make sure the tests are passing when cvcuda is not installed!

[AntoineSimoulin]

@justincdavis it seems that _import_cvcuda().Tensor is still raising an error if cvcuda is not installed. Maybe we can just use cvcuda.Tensor here and see if this works better?

[justincdavis]

Thank you for pointing this out! I replaced the actual cvcuda.Tensor type with the string "cvcuda.Tensor", then inside the function we resolve the cvcuda.Tensor type if we have the corresponding string. LMK if this looks like a reasonable solution!

[justincdavis]

Following up from my comment in the _normalize_cvcuda function itself. CV-CUDA requires that the mean and scale tensors be on-device when we call cvcuda.normalize. This means that a host->device memcpy must occur twice for each normalize call when using CV-CUDA backend. We could attempt to reduce the impact of this by having a helper function which creates the tuple[cvcuda.Tensor, cvcuda.Tensor] from the mean/std parameters. Based on what I see in the codebase, this seems like it would be a new feature present in torchvision for a functional transform.

# CV-CUDA requires float32 tensors for the mean/std parameters
# at small batchs, this is costly relative to normalize operation
# if CV-CUDA is known to be a backend, could optimize this
# For Normalize class:
# by creating tensors at class initialization time
# For functional API:
# by storing cached tensors in helper function with functools.lru_cache (would it even be worth it?)
# Since CV-CUDA is 1) not default backend, 2) only strictly faster at large batch size, ignore

[AntoineSimoulin]

Hey @justincdavis, looking good to me. I don't think the failing test is related to this PR. Seems like a false positive alert to me! Can you sign our Contributor License Agreement (c.f. meta-cla bot comment in the discussion)?