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Support v2.functional.gaussian_blur backprop #8486

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merged 3 commits into from
Jul 29, 2024

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andreasfloros
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@andreasfloros andreasfloros commented Jun 8, 2024

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')

cc @vfdev-5

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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.

❌ 4 New Failures, 16 Unrelated Failures

As of commit 648efae with merge base 15a69ca (image):

NEW FAILURES - The following jobs have failed:

FLAKY - The following job failed but was likely due to flakiness present on trunk:

BROKEN TRUNK - The following jobs failed but was present on the merge base:

👉 Rebase onto the `viable/strict` branch to avoid these failures

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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@NicolasHug NicolasHug merged commit ee6c3d0 into pytorch:main Jul 29, 2024
53 of 77 checks passed
@andreasfloros andreasfloros deleted the v2-gaussian-blur-backprop branch July 29, 2024 15:46
facebook-github-bot pushed a commit that referenced this pull request Aug 2, 2024
Summary: Co-authored-by: Nicolas Hug <[email protected]>

Reviewed By: vmoens

Differential Revision: D60596233

fbshipit-source-id: aa7d8a3295cdc206c8503d01da3c48821512898c
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Let v2.functional.gaussian_blur backprop through sigma parameter
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