Add fast image processor for ViViT #40209
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Hi @yonigozlan! Could you please take a look. Thank you. |
yonigozlan
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Sep 8, 2025
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Hi @msminhas93, thanks for contributing, a few things can be simplified!
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| def make_batched(videos) -> list[list]: | ||
| """Make videos batched for processing.""" | ||
| if isinstance(videos, (list, tuple)) and isinstance(videos[0], (list, tuple)): | ||
| return videos | ||
| elif isinstance(videos, (list, tuple)): | ||
| return [videos] | ||
| else: | ||
| return [[videos]] | ||
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Contributor
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We have make_nested_list_of_images in image_utils for that
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| processed_videos = [] | ||
| for video in images: # images is a list of videos, each video is a list of frames | ||
| # Process each frame in the video | ||
| processed_frames = [] | ||
| for frame in video: | ||
| # Apply transformations to individual frame | ||
| if do_resize: | ||
| frame = self.resize(image=frame, size=size, interpolation=interpolation) | ||
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| if do_center_crop: | ||
| frame = self.center_crop(frame, crop_size) | ||
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| # Handle rescaling with offset parameter | ||
| if do_rescale: | ||
| frame = self.rescale(frame, rescale_factor, offset=offset) | ||
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| # Handle normalization | ||
| if do_normalize: | ||
| frame = self.normalize(frame, image_mean, image_std) | ||
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| processed_frames.append(frame) | ||
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| # Stack frames for this video | ||
| if return_tensors: | ||
| video_tensor = torch.stack(processed_frames, dim=0) | ||
| processed_videos.append(video_tensor) | ||
| else: | ||
| processed_videos.append(processed_frames) | ||
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| # Stack videos if returning tensors | ||
| if return_tensors: | ||
| processed_videos = torch.stack(processed_videos, dim=0) | ||
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| return BatchFeature(data={"pixel_values": processed_videos}, tensor_type=return_tensors) |
Contributor
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You can process fully by batch still. just use the code from the parent class, and set is_nested=True in group_images_by_shape and reorder_images
Contributor
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[For maintainers] Suggested jobs to run (before merge) run-slow: auto, vivit |
Author
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Thank you for the comments! I've updated my PR @yonigozlan |
Author
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@yonigozlan If you could please look at the PR that'll be awesome Thank you! |
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What does this PR do?
This PR implements a fast PyTorch image processor for ViViT (
VivitImageProcessorFast) that extends the existingBaseImageProcessorFastclass. The new processor provides optimized image processing capabilities specifically designed for video understanding tasks using the ViViT model.Key Features
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Who can review?
@yonigozlan