Add ViViT (#22518)
* Add model * Add ability to get classification head weights * Add docs * Add imports to __init__.py * Run style * Fix imports and add mdx doc * Run style * Fix copyright * Fix config docstring * Remove imports of ViViTLayer and load_tf_weights_in_vivit * Remove FeatureExtractor and replace with ImageProcessor everywhere * Remove ViViTForPreTraining from vivit.mdx * Change ViViT -> Vivit everywhere * Add model_doc to _toctree.yml * Replace tuples with lists in arguments of VivitConfig * Rename patch_size to tubelet_size in TubeletEmbeddings * Fix checkpoint names * Add tests * Remove unused num_frames * Fix imports for VivitImageProcessor * Minor fixes * Decrease number of frames in VivitModelTester from 32 to 16 * Decrease number of frames in VivitModelTester from 16 to 8 * Add initialization for pos embeddings * Rename Vivit -> ViViT in some places * Fix docstring and formatting * Rename TubeletEmbeddings -> VivitTubeletEmbeddings * Remove load_tf_weights_in_vivit * Change checkpoint name * Remove Vivit _TOKENIZER_FOR_DOC * Fix * Fix VivitTubeletEmbeddings and pass config object as parameter * Use image_size and num_frames instead of video_size * Change conversion script and fix differences with the orig implementation * Fix docstrings * Add attention head pruning * Run style and fixup * Fix tests * Add ViViT to video_classification.mdx * Save processor in conversion script * Fix * Add image processor test * Run fixup and style * Run fix-copies * Update tests/models/vivit/test_modeling_vivit.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update tests/models/vivit/test_modeling_vivit.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/vivit/modeling_vivit.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Use PyAV instead of decord * Add unittest.skip * Run style * Remove unneeded test * Update docs/source/en/model_doc/vivit.mdx Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/vivit/configuration_vivit.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/vivit/modeling_vivit.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/vivit/image_processing_vivit.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/vivit/modeling_vivit.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/vivit/modeling_vivit.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/vivit/image_processing_vivit.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/vivit/modeling_vivit.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Add model * Add docs * Run style * Fix imports and add mdx doc * Remove FeatureExtractor and replace with ImageProcessor everywhere * Change ViViT -> Vivit everywhere * Rename Vivit -> ViViT in some places * Update src/transformers/models/vivit/image_processing_vivit.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Run make style * Remove inputs save * Fix image processor * Fix * Run `make style` * Decrease parameters of VivitModelTester * Decrease tubelet size * Rename vivit.mdx * Update src/transformers/models/vivit/image_processing_vivit.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/vivit/image_processing_vivit.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Update src/transformers/models/vivit/image_processing_vivit.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Fix default values in image_processing_vivit.py --------- Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
This commit is contained in:
Jegor Kitškerkin
@@ -538,6 +538,8 @@
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title: ViTMAE
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- local: model_doc/vit_msn
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title: ViTMSN
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- local: model_doc/vivit
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title: ViViT
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- local: model_doc/yolos
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title: YOLOS
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title: Vision models
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@@ -246,6 +246,7 @@ The documentation is organized into five sections:
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1. **[ViT Hybrid](model_doc/vit_hybrid)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
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1. **[ViTMAE](model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
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1. **[ViTMSN](model_doc/vit_msn)** (from Meta AI) released with the paper [Masked Siamese Networks for Label-Efficient Learning](https://arxiv.org/abs/2204.07141) by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas.
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1. **[ViViT](model_doc/vivit)** (from Google Research) released with the paper [ViViT: A Video Vision Transformer](https://arxiv.org/abs/2103.15691) by Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, Cordelia Schmid.
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1. **[Wav2Vec2](model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
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1. **[Wav2Vec2-Conformer](model_doc/wav2vec2-conformer)** (from Facebook AI) released with the paper [FAIRSEQ S2T: Fast Speech-to-Text Modeling with FAIRSEQ](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino.
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1. **[Wav2Vec2Phoneme](model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
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@@ -457,6 +458,7 @@ Flax), PyTorch, and/or TensorFlow.
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| ViT Hybrid | ❌ | ❌ | ✅ | ❌ | ❌ |
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| ViTMAE | ❌ | ❌ | ✅ | ✅ | ❌ |
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| ViTMSN | ❌ | ❌ | ✅ | ❌ | ❌ |
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| ViViT | ❌ | ❌ | ✅ | ❌ | ❌ |
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| Wav2Vec2 | ✅ | ❌ | ✅ | ✅ | ✅ |
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| Wav2Vec2-Conformer | ❌ | ❌ | ✅ | ❌ | ❌ |
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| WavLM | ❌ | ❌ | ✅ | ❌ | ❌ |
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44
docs/source/en/model_doc/vivit.md
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docs/source/en/model_doc/vivit.md
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<!--Copyright 2023 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# Video Vision Transformer (ViViT)
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## Overview
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The Vivit model was proposed in [ViViT: A Video Vision Transformer](https://arxiv.org/abs/2103.15691) by Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, Cordelia Schmid.
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The paper proposes one of the first successful pure-transformer based set of models for video understanding.
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The abstract from the paper is the following:
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*We present pure-transformer based models for video classification, drawing upon the recent success of such models in image classification. Our model extracts spatio-temporal tokens from the input video, which are then encoded by a series of transformer layers. In order to handle the long sequences of tokens encountered in video, we propose several, efficient variants of our model which factorise the spatial- and temporal-dimensions of the input. Although transformer-based models are known to only be effective when large training datasets are available, we show how we can effectively regularise the model during training and leverage pretrained image models to be able to train on comparatively small datasets. We conduct thorough ablation studies, and achieve state-of-the-art results on multiple video classification benchmarks including Kinetics 400 and 600, Epic Kitchens, Something-Something v2 and Moments in Time, outperforming prior methods based on deep 3D convolutional networks.*
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This model was contributed by [jegormeister](https://huggingface.co/jegormeister). The original code (written in JAX) can be found [here](https://github.com/google-research/scenic/tree/main/scenic/projects/vivit).
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## VivitConfig
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[[autodoc]] VivitConfig
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## VivitImageProcessor
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[[autodoc]] VivitImageProcessor
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- preprocess
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## VivitModel
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[[autodoc]] VivitModel
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- forward
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## VivitForVideoClassification
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[[autodoc]] transformers.VivitForVideoClassification
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- forward
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@@ -30,7 +30,7 @@ The task illustrated in this tutorial is supported by the following model archit
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<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
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[TimeSformer](../model_doc/timesformer), [VideoMAE](../model_doc/videomae)
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[TimeSformer](../model_doc/timesformer), [VideoMAE](../model_doc/videomae), [ViViT](../model_doc/vivit)
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<!--End of the generated tip-->
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