Improve vision models #17731
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Improve vision models #17731
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This was referenced Jun 22, 2022
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src/transformers/models/data2vec/modeling_tf_data2vec_vision.py
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* Improve vision models * Add a lot of improvements * Remove to_2tuple from swin tests * Fix TF Swin * Fix more tests * Fix copies * Improve more models * Fix ViTMAE test * Add channel check for TF models * Add proper channel check for TF models * Apply suggestion from code review * Apply suggestions from code review * Add channel check for Flax models, apply suggestion * Fix bug * Add tests for greyscale images * Add test for interpolation of pos encodigns Co-authored-by: Niels Rogge <[email protected]>
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* Improve vision models * Add a lot of improvements * Remove to_2tuple from swin tests * Fix TF Swin * Fix more tests * Fix copies * Improve more models * Fix ViTMAE test * Add channel check for TF models * Add proper channel check for TF models * Apply suggestion from code review * Apply suggestions from code review * Add channel check for Flax models, apply suggestion * Fix bug * Add tests for greyscale images * Add test for interpolation of pos encodigns Co-authored-by: Niels Rogge <[email protected]>
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* Initial TF DeiT implementation * Fix copies naming issues * Fix up + docs * Properly same main layer * Name layers properly * Initial TF DeiT implementation * Fix copies naming issues * Fix up + docs * Properly same main layer * Name layers properly * Fixup * Fix import * Fix import * Fix import * Fix weight loading for tests whilst not on hub * Add doc tests and remove to_2tuple * Add back to_2tuple Removing to_2tuple results in many downstream changes needed because of the copies checks * Incorporate updates in Improve vision models #17731 PR * Don't hard code num_channels * Copy PyTorch DeiT embeddings and remove pytorch operations with mask * Fix patch embeddings & tidy up * Update PixelShuffle to move logic into class layer * Update doc strings - remove PT references * Use NHWC format in internal layers * Fix up * Use linear activation layer * Remove unused import * Apply suggestions from code review Co-authored-by: Sylvain Gugger <[email protected]> Co-authored-by: NielsRogge <[email protected]> Co-authored-by: NielsRogge <[email protected]> Co-authored-by: Sylvain Gugger <[email protected]> * Move dataclass to top of file * Remove from_pt now weights on hub * Fixup Co-authored-by: NielsRogge <[email protected]> Co-authored-by: Sylvain Gugger <[email protected]> Co-authored-by: Amy Roberts <[email protected]>
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* Initial TF DeiT implementation * Fix copies naming issues * Fix up + docs * Properly same main layer * Name layers properly * Initial TF DeiT implementation * Fix copies naming issues * Fix up + docs * Properly same main layer * Name layers properly * Fixup * Fix import * Fix import * Fix import * Fix weight loading for tests whilst not on hub * Add doc tests and remove to_2tuple * Add back to_2tuple Removing to_2tuple results in many downstream changes needed because of the copies checks * Incorporate updates in Improve vision models huggingface#17731 PR * Don't hard code num_channels * Copy PyTorch DeiT embeddings and remove pytorch operations with mask * Fix patch embeddings & tidy up * Update PixelShuffle to move logic into class layer * Update doc strings - remove PT references * Use NHWC format in internal layers * Fix up * Use linear activation layer * Remove unused import * Apply suggestions from code review Co-authored-by: Sylvain Gugger <[email protected]> Co-authored-by: NielsRogge <[email protected]> Co-authored-by: NielsRogge <[email protected]> Co-authored-by: Sylvain Gugger <[email protected]> * Move dataclass to top of file * Remove from_pt now weights on hub * Fixup Co-authored-by: NielsRogge <[email protected]> Co-authored-by: Sylvain Gugger <[email protected]> Co-authored-by: Amy Roberts <[email protected]>
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What does this PR do?
This PR improves the vision models by:
to_2tupleconfig.num_channelsconfig.num_channelsforxxxForMaskedImageModelingmodels (fixes SimMIM output num_channels should not be hardcoded #17727)config.num_channelsin Flax models (ViT, BEiT)To do: