[Fast image processors] Improve handling of image-like inputs other than images (segmentation_maps) #39489
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ArthurZucker
approved these changes
Jul 21, 2025
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| segmentation_maps_kwargs = kwargs.copy() | ||
| segmentation_maps_kwargs["do_normalize"] = False | ||
| segmentation_maps_kwargs["do_rescale"] = False | ||
| segmentation_maps_kwargs["input_data_format"] = ChannelDimension.FIRST | ||
| # Nearest interpolation is used for segmentation maps instead of BILINEAR. | ||
| segmentation_maps_kwargs["interpolation"] = pil_torch_interpolation_mapping[PILImageResampling.NEAREST] | ||
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I would pass these explicitly instead of updating them but a small nit maybe complicated
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I would still need to remove them from the kwargs as they have the default values for image processing there, so not sure if that simplifies...
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@ArthurZucker Should be 100% BC 🤗 |
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[For maintainers] Suggested jobs to run (before merge) run-slow: beit, dpt, eomt, idefics2, idefics3, llava_next, llava_onevision, mobilenet_v2, mobilevit, qwen2_vl, sam, smolvlm, vitmatte |
zucchini-nlp
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Jul 22, 2025
…han images (segmentation_maps) (huggingface#39489) * improve handlike of other image-like inputs in fast image processors * fix issues with _prepare_images_structure * update sam image processor fast * use dict update
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What does this PR do?
As the title says, unbloats (😉) a lot of the fast processing code for models needing to processed segmentation maps, trimaps, depth_maps etc.