Erase CV-CUDA Backend#9304
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justincdavis wants to merge 20 commits intopytorch:mainfrom
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…memcpy hack, need to figure out
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/vision/9304
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zy1git
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Jan 15, 2026
| # packed types (_2S32, _3S32) need to be copied into pre-allocated tensors | ||
| # torch does not support these packed types directly, so we create a helper function | ||
| # which will enable torch copy into the data directly (by overriding type/strides info) | ||
| def _to_torch(cv_tensor: cvcuda.Tensor, shape: tuple[int, ...], typestr: str) -> torch.Tensor: |
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Consider moving _to_torch to module level — currently it's recreated on every function call.
Not a performance concern for typical usage, but cleaner for readability.
| return erase_image(video, i=i, j=j, h=h, w=w, v=v, inplace=inplace) | ||
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| def _erase_image_cvcuda( |
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Need to take a closer look at the implementation @NicolasHug
| # CV-CUDA doesnt have same random distribution as torchvision | ||
| # it uses its own seeding, but we have determinism | ||
| # set seed with torch.randint in the kernel | ||
| assert_close(actual, expected, rtol=0, atol=256) |
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need investigation for atol = 256
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Summary
Add the CV-CUDA for
eraseTesting
python3 -m pytest test/test_transforms_v2.py::TestErase