GaussianBlur CV-CUDA Backend

[justincdavis]

Summary

Implement the CV-CUDA backend kernel for gaussian_blur

How to use

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

cv_tensor = cvcuda.Tensor((1, 224, 224, 3), cvcuda.Type.U8, cvcuda.TensorLayout.NHWC)
# dispatched to F.gaussian_blur_cvcuda
blurred = F.gaussian_blur(cv_tensor, (5, 5))

[pytorch-bot]

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[AntoineSimoulin]

Thanks for the PR @justincdavis. Left a few comments, looking good otherwise!

[AntoineSimoulin]

Why setting atol=0.3 here?

[justincdavis]

Good question! I added a comment on atol=0.3, most likely from floating point differences between the underlying filter2d in CV-CUDA compared to torch.conv2d. Let me know if you want more explanation and/or something else here.

[AntoineSimoulin]

Should we set it as in test_functional_image_correctness with torch.testing.assert_close(actual, expected, rtol=0, atol=1) for consistency?

[justincdavis]

@AntoineSimoulin I ended up rewriting the test setup, and moved all the tests into a single block. Both CV-CUDA and torchvision share the same assert statement now. LMK if you think it looks like a good change.