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Support v2.functional.gaussian_blur backprop #8486

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Closes #8450

import torch
from torchvision.transforms.functional import gaussian_blur
from torchvision.transforms.v2.functional import gaussian_blur as gaussian_blur_v2

devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
k = 15

for device in devices:
    for blur in [gaussian_blur, gaussian_blur_v2]:
        torch.manual_seed(0)
        img = torch.randn(1, 3, 256, 256, device=device)
        s = torch.tensor(0.3 * ((5 - 1) * 0.5 - 1) + 0.8, requires_grad=True, device=device)
        blurred = blur(img, k, [s])
        blurred.mean().backward()
        print(s.grad)
tensor(-2.1697e-06)
tensor(-2.1695e-06)
tensor(4.1030e-06, device='cuda:0')
tensor(4.1030e-06, device='cuda:0')

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pytorch-bot bot commented Jun 8, 2024

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/vision/8486

Note: Links to docs will display an error until the docs builds have been completed.

❌ 3 New Failures

As of commit aec94e2 with merge base f96c42f (image):

NEW FAILURES - The following jobs have failed:

This comment was automatically generated by Dr. CI and updates every 15 minutes.

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@andreasfloros
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@NicolasHug I think the errors are unrelated, could you take a look?

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Let v2.functional.gaussian_blur backprop through sigma parameter
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