Let Normalize() and RandomPhotometricDistort return datapoints instead of tensors #7113
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NicolasHug
commented
Jan 19, 2023
| output = inpt.wrap_like(inpt, output, color_space=datapoints.ColorSpace.OTHER) # type: ignore[arg-type] | ||
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| elif isinstance(inpt, PIL.Image.Image): | ||
| if isinstance(orig_inpt, PIL.Image.Image): |
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I suspect the original version had a bug: at this point inpt was always a Tensor because of line 122, and so the condition on line 130 was never True and this function would always return a Tensor even if a PIL image was passed
pmeier
approved these changes
Jan 20, 2023
| @@ -82,6 +82,7 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any: | |||
| return output | |||
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| # TODO: Are there tests for this class? | |||
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I don't think so. We have a pretty good grip on the functional testing, but so far the transforms testing is the wild west. Seems we have forgotten this completely.
| output = inpt.wrap_like(inpt, output, color_space=datapoints.ColorSpace.OTHER) # type: ignore[arg-type] | ||
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| elif isinstance(inpt, PIL.Image.Image): | ||
| if isinstance(orig_inpt, PIL.Image.Image): |
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Hey @NicolasHug! You merged this PR, but no labels were added. The list of valid labels is available at https://github.com/pytorch/vision/blob/main/.github/process_commit.py |
This was referenced Jan 20, 2023
facebook-github-bot
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Jan 24, 2023
…ts instead of tensors (#7113) Reviewed By: YosuaMichael Differential Revision: D42706907 fbshipit-source-id: a7b7487ab8563f8a43a0ebb84df19579ccd35fe1
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See #7092 (comment) for the rationale.
There are some TODO comments on which I'd love some input
cc @bjuncek @pmeier