From 56baa03380fc17d85705240ebbc57c075a9b3f23 Mon Sep 17 00:00:00 2001 From: StevenBucaille Date: Tue, 19 Mar 2024 15:43:02 +0100 Subject: [PATCH] Implementation of SuperPoint and AutoModelForKeypointDetection (#28966) * Added SuperPoint docs * Added tests * Removed commented part * Commit to create and fix add_superpoint branch with a new branch * Fixed dummy_pt_objects * Committed missing files * Fixed README.md * Apply suggestions from code review Fixed small changes Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Moved ImagePointDescriptionOutput from modeling_outputs.py to modeling_superpoint.py * Removed AutoModelForKeypointDetection and related stuff * Fixed inconsistencies in image_processing_superpoint.py * Moved infer_on_model logic simply in test_inference * Fixed bugs, added labels to forward method with checks whether it is properly a None value, also added tests about this logic in test_modeling_superpoint.py * Added tests to SuperPointImageProcessor to ensure that images are properly converted to grayscale * Removed remaining mentions of MODEL_FOR_KEYPOINT_DETECTION_MAPPING * Apply suggestions from code review Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Fixed from (w, h) to (h, w) as input for tests * Removed unnecessary condition * Moved last_hidden_state to be the first returned * Moved last_hidden_state to be the first returned (bis) * Moved last_hidden_state to be the first returned (ter) * Switched image_width and image_height in tests to match recent changes * Added config as first SuperPointConvBlock init argument * Reordered README's after merge * Added missing first config argument to SuperPointConvBlock instantiations * Removed formatting error * Added SuperPoint to README's de, pt-br, ru, te and vi * Checked out README_fr.md * Fixed README_fr.md * Test fix README_fr.md * Test fix README_fr.md * Last make fix-copies ! * Updated checkpoint path * Removed unused SuperPoint doc * Added missing image * Update src/transformers/models/superpoint/modeling_superpoint.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Removed unnecessary import * Update src/transformers/models/superpoint/modeling_superpoint.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Added SuperPoint to _toctree.yml --------- Co-authored-by: steven Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> Co-authored-by: Steven Bucaille --- README.md | 1 + README_de.md | 1 + README_es.md | 1 + README_fr.md | 3 +- README_hd.md | 1 + README_ja.md | 1 + README_ko.md | 1 + README_pt-br.md | 1 + README_ru.md | 1 + README_te.md | 1 + README_vi.md | 1 + README_zh-hans.md | 1 + README_zh-hant.md | 1 + docs/source/en/_toctree.yml | 2 + docs/source/en/index.md | 1 + docs/source/en/model_doc/superpoint.md | 122 +++++ src/transformers/__init__.py | 16 + src/transformers/models/__init__.py | 1 + src/transformers/models/auto/__init__.py | 2 + .../models/auto/configuration_auto.py | 3 + src/transformers/models/auto/modeling_auto.py | 1 + .../models/superpoint/__init__.py | 77 +++ .../superpoint/configuration_superpoint.py | 91 ++++ .../convert_superpoint_to_pytorch.py | 175 ++++++ .../superpoint/image_processing_superpoint.py | 272 ++++++++++ .../models/superpoint/modeling_superpoint.py | 507 ++++++++++++++++++ src/transformers/utils/dummy_pt_objects.py | 17 + .../utils/dummy_vision_objects.py | 7 + .../tests_samples/COCO/000000004016.png | Bin 0 -> 636411 bytes tests/models/superpoint/__init__.py | 0 .../test_image_processing_superpoint.py | 111 ++++ .../superpoint/test_modeling_superpoint.py | 307 +++++++++++ 32 files changed, 1726 insertions(+), 1 deletion(-) create mode 100644 docs/source/en/model_doc/superpoint.md create mode 100644 src/transformers/models/superpoint/__init__.py create mode 100644 src/transformers/models/superpoint/configuration_superpoint.py create mode 100644 src/transformers/models/superpoint/convert_superpoint_to_pytorch.py create mode 100644 src/transformers/models/superpoint/image_processing_superpoint.py create mode 100644 src/transformers/models/superpoint/modeling_superpoint.py create mode 100644 tests/fixtures/tests_samples/COCO/000000004016.png create mode 100644 tests/models/superpoint/__init__.py create mode 100644 tests/models/superpoint/test_image_processing_superpoint.py create mode 100644 tests/models/superpoint/test_modeling_superpoint.py diff --git a/README.md b/README.md index fdbd59883dd..e2d02db2eeb 100644 --- a/README.md +++ b/README.md @@ -498,6 +498,7 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer. 1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** (from Stability AI) released with the paper [StableLM 3B 4E1T (Technical Report)](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu. 1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (from BigCode team) released with the paper [StarCoder 2 and The Stack v2: The Next Generation](https://arxiv.org/abs/2402.19173) by Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, Tianyang Liu, Max Tian, Denis Kocetkov, Arthur Zucker, Younes Belkada, Zijian Wang, Qian Liu, Dmitry Abulkhanov, Indraneil Paul, Zhuang Li, Wen-Ding Li, Megan Risdal, Jia Li, Jian Zhu, Terry Yue Zhuo, Evgenii Zheltonozhskii, Nii Osae Osae Dade, Wenhao Yu, Lucas Krauß, Naman Jain, Yixuan Su, Xuanli He, Manan Dey, Edoardo Abati, Yekun Chai, Niklas Muennighoff, Xiangru Tang, Muhtasham Oblokulov, Christopher Akiki, Marc Marone, Chenghao Mou, Mayank Mishra, Alex Gu, Binyuan Hui, Tri Dao, Armel Zebaze, Olivier Dehaene, Nicolas Patry, Canwen Xu, Julian McAuley, Han Hu, Torsten Scholak, Sebastien Paquet, Jennifer Robinson, Carolyn Jane Anderson, Nicolas Chapados, Mostofa Patwary, Nima Tajbakhsh, Yacine Jernite, Carlos Muñoz Ferrandis, Lingming Zhang, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries. +1. **[SuperPoint](https://huggingface.co/docs/transformers/main/model_doc/superpoint)** (from MagicLeap) released with the paper [SuperPoint: Self-Supervised Interest Point Detection and Description](https://arxiv.org/abs/1712.07629) by Daniel DeTone, Tomasz Malisiewicz and Andrew Rabinovich. 1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (from MBZUAI) released with the paper [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan. 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo. 1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo. diff --git a/README_de.md b/README_de.md index a1a01676719..ebcb3a16d5f 100644 --- a/README_de.md +++ b/README_de.md @@ -494,6 +494,7 @@ Aktuelle Anzahl der Checkpoints: ![](https://img.shields.io/endpoint?url=https:/ 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer. 1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** (from Stability AI) released with the paper [StableLM 3B 4E1T (Technical Report) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu. 1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (from BigCode team) released with the paper [StarCoder 2 and The Stack v2: The Next Generation](https://arxiv.org/abs/2402.19173) by Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, Tianyang Liu, Max Tian, Denis Kocetkov, Arthur Zucker, Younes Belkada, Zijian Wang, Qian Liu, Dmitry Abulkhanov, Indraneil Paul, Zhuang Li, Wen-Ding Li, Megan Risdal, Jia Li, Jian Zhu, Terry Yue Zhuo, Evgenii Zheltonozhskii, Nii Osae Osae Dade, Wenhao Yu, Lucas Krauß, Naman Jain, Yixuan Su, Xuanli He, Manan Dey, Edoardo Abati, Yekun Chai, Niklas Muennighoff, Xiangru Tang, Muhtasham Oblokulov, Christopher Akiki, Marc Marone, Chenghao Mou, Mayank Mishra, Alex Gu, Binyuan Hui, Tri Dao, Armel Zebaze, Olivier Dehaene, Nicolas Patry, Canwen Xu, Julian McAuley, Han Hu, Torsten Scholak, Sebastien Paquet, Jennifer Robinson, Carolyn Jane Anderson, Nicolas Chapados, Mostofa Patwary, Nima Tajbakhsh, Yacine Jernite, Carlos Muñoz Ferrandis, Lingming Zhang, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries. +1. **[SuperPoint](https://huggingface.co/docs/transformers/main/model_doc/superpoint)** (from MagicLeap) released with the paper [SuperPoint: Self-Supervised Interest Point Detection and Description](https://arxiv.org/abs/1712.07629) by Daniel DeTone, Tomasz Malisiewicz and Andrew Rabinovich. 1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (from MBZUAI) released with the paper [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan. 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo. 1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo. diff --git a/README_es.md b/README_es.md index 61a5f284b88..7c7800b1145 100644 --- a/README_es.md +++ b/README_es.md @@ -471,6 +471,7 @@ Número actual de puntos de control: ![](https://img.shields.io/endpoint?url=htt 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer. 1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** (from Stability AI) released with the paper [StableLM 3B 4E1T (Technical Report)](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu. 1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (from BigCode team) released with the paper [StarCoder 2 and The Stack v2: The Next Generation](https://arxiv.org/abs/2402.19173) by Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, Tianyang Liu, Max Tian, Denis Kocetkov, Arthur Zucker, Younes Belkada, Zijian Wang, Qian Liu, Dmitry Abulkhanov, Indraneil Paul, Zhuang Li, Wen-Ding Li, Megan Risdal, Jia Li, Jian Zhu, Terry Yue Zhuo, Evgenii Zheltonozhskii, Nii Osae Osae Dade, Wenhao Yu, Lucas Krauß, Naman Jain, Yixuan Su, Xuanli He, Manan Dey, Edoardo Abati, Yekun Chai, Niklas Muennighoff, Xiangru Tang, Muhtasham Oblokulov, Christopher Akiki, Marc Marone, Chenghao Mou, Mayank Mishra, Alex Gu, Binyuan Hui, Tri Dao, Armel Zebaze, Olivier Dehaene, Nicolas Patry, Canwen Xu, Julian McAuley, Han Hu, Torsten Scholak, Sebastien Paquet, Jennifer Robinson, Carolyn Jane Anderson, Nicolas Chapados, Mostofa Patwary, Nima Tajbakhsh, Yacine Jernite, Carlos Muñoz Ferrandis, Lingming Zhang, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries. +1. **[SuperPoint](https://huggingface.co/docs/transformers/main/model_doc/superpoint)** (from MagicLeap) released with the paper [SuperPoint: Self-Supervised Interest Point Detection and Description](https://arxiv.org/abs/1712.07629) by Daniel DeTone, Tomasz Malisiewicz and Andrew Rabinovich. 1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (from MBZUAI) released with the paper [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan. 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo. 1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo. diff --git a/README_fr.md b/README_fr.md index ab0a790c284..f131e02daaf 100644 --- a/README_fr.md +++ b/README_fr.md @@ -143,7 +143,7 @@ Pour utiliser immédiatement un modèle sur une entrée donnée (texte, image, a # Allouer un pipeline pour l'analyse de sentiment >>> classifieur = pipeline('sentiment-analysis') ->>> classifieur('Nous sommes très heureux d'introduire le pipeline dans le référentiel transformers.') +>>> classifieur("Nous sommes très heureux d'introduire le pipeline dans le référentiel transformers.") [{'label': 'POSITIF', 'score': 0.9996980428695679}] ``` @@ -492,6 +492,7 @@ Nombre actuel de points de contrôle : ![](https://img.shields.io/endpoint?url=h 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (de Berkeley) a été publié dans l'article [SqueezeBERT : Que l'apprentissage automatique peut-il apprendre au traitement du langage naturel sur les réseaux neuronaux efficaces ?](https://arxiv.org/abs/2006.11316) par Forrest N. Iandola, Albert E. Shaw, Ravi Krishna et Kurt W. Keutzer. 1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** (from Stability AI) released with the paper [StableLM 3B 4E1T (Technical Report)](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu. 1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (from BigCode team) released with the paper [StarCoder 2 and The Stack v2: The Next Generation](https://arxiv.org/abs/2402.19173) by Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, Tianyang Liu, Max Tian, Denis Kocetkov, Arthur Zucker, Younes Belkada, Zijian Wang, Qian Liu, Dmitry Abulkhanov, Indraneil Paul, Zhuang Li, Wen-Ding Li, Megan Risdal, Jia Li, Jian Zhu, Terry Yue Zhuo, Evgenii Zheltonozhskii, Nii Osae Osae Dade, Wenhao Yu, Lucas Krauß, Naman Jain, Yixuan Su, Xuanli He, Manan Dey, Edoardo Abati, Yekun Chai, Niklas Muennighoff, Xiangru Tang, Muhtasham Oblokulov, Christopher Akiki, Marc Marone, Chenghao Mou, Mayank Mishra, Alex Gu, Binyuan Hui, Tri Dao, Armel Zebaze, Olivier Dehaene, Nicolas Patry, Canwen Xu, Julian McAuley, Han Hu, Torsten Scholak, Sebastien Paquet, Jennifer Robinson, Carolyn Jane Anderson, Nicolas Chapados, Mostofa Patwary, Nima Tajbakhsh, Yacine Jernite, Carlos Muñoz Ferrandis, Lingming Zhang, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries. +1. **[SuperPoint](https://huggingface.co/docs/transformers/main/model_doc/superpoint)** (de MagicLeap) publié dans l'article [SuperPoint: Self-Supervised Interest Point Detection and Description](https://arxiv.org/abs/1712.07629) parDaniel DeTone, Tomasz Malisiewicz and Andrew Rabinovich. 1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (de MBZUAI) a été publié dans l'article [SwiftFormer : Attention additive efficace pour les applications de vision mobile en temps réel basées sur des transformateurs](https://arxiv.org/abs/2303.15446) par Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan. 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (de Microsoft) a été publié dans l'article [Swin Transformer : Transformateur hiérarchique de la vision utilisant des fenêtres décalées](https://arxiv.org/abs/2103.14030) par Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo. 1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (de Microsoft) a été publié dans l'article [Swin Transformer V2 : Augmentation de la capacité et de la résolution](https://arxiv.org/abs/2111.09883) par Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo. diff --git a/README_hd.md b/README_hd.md index 4c1b6a419e4..327bb103584 100644 --- a/README_hd.md +++ b/README_hd.md @@ -445,6 +445,7 @@ conda install conda-forge::transformers 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (बर्कले से) कागज के साथ [SqueezeBERT: कुशल तंत्रिका नेटवर्क के बारे में NLP को कंप्यूटर विज़न क्या सिखा सकता है?](https://arxiv.org/abs/2006.11316) फॉरेस्ट एन. इनडोला, अल्बर्ट ई. शॉ, रवि कृष्णा, और कर्ट डब्ल्यू. केटज़र द्वारा। 1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** (from Stability AI) released with the paper [StableLM 3B 4E1T (Technical Report)](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu. 1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (from BigCode team) released with the paper [StarCoder 2 and The Stack v2: The Next Generation](https://arxiv.org/abs/2402.19173) by Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, Tianyang Liu, Max Tian, Denis Kocetkov, Arthur Zucker, Younes Belkada, Zijian Wang, Qian Liu, Dmitry Abulkhanov, Indraneil Paul, Zhuang Li, Wen-Ding Li, Megan Risdal, Jia Li, Jian Zhu, Terry Yue Zhuo, Evgenii Zheltonozhskii, Nii Osae Osae Dade, Wenhao Yu, Lucas Krauß, Naman Jain, Yixuan Su, Xuanli He, Manan Dey, Edoardo Abati, Yekun Chai, Niklas Muennighoff, Xiangru Tang, Muhtasham Oblokulov, Christopher Akiki, Marc Marone, Chenghao Mou, Mayank Mishra, Alex Gu, Binyuan Hui, Tri Dao, Armel Zebaze, Olivier Dehaene, Nicolas Patry, Canwen Xu, Julian McAuley, Han Hu, Torsten Scholak, Sebastien Paquet, Jennifer Robinson, Carolyn Jane Anderson, Nicolas Chapados, Mostofa Patwary, Nima Tajbakhsh, Yacine Jernite, Carlos Muñoz Ferrandis, Lingming Zhang, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries. +1. **[SuperPoint](https://huggingface.co/docs/transformers/main/model_doc/superpoint)** (from MagicLeap) released with the paper [SuperPoint: Self-Supervised Interest Point Detection and Description](https://arxiv.org/abs/1712.07629) by Daniel DeTone, Tomasz Malisiewicz and Andrew Rabinovich. 1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (MBZUAI से) Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan. द्वाराअनुसंधान पत्र [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) के साथ जारी किया गया 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (माइक्रोसॉफ्ट से) साथ में कागज [स्वाइन ट्रांसफॉर्मर: शिफ्टेड विंडोज का उपयोग कर पदानुक्रमित विजन ट्रांसफॉर्मर](https://arxiv.org/abs/2103.14030) ज़ी लियू, युटोंग लिन, यू काओ, हान हू, यिक्सुआन वेई, झेंग झांग, स्टीफन लिन, बैनिंग गुओ द्वारा। 1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (Microsoft से) साथ वाला पेपर [Swin Transformer V2: स्केलिंग अप कैपेसिटी एंड रेजोल्यूशन](https://arxiv.org/abs/2111.09883) ज़ी लियू, हान हू, युटोंग लिन, ज़ुलिआंग याओ, ज़ेंडा ज़ी, यिक्सुआन वेई, जिया निंग, यू काओ, झेंग झांग, ली डोंग, फुरु वेई, बैनिंग गुओ द्वारा। diff --git a/README_ja.md b/README_ja.md index 7efc8cd0570..fff8572a00d 100644 --- a/README_ja.md +++ b/README_ja.md @@ -505,6 +505,7 @@ Flax、PyTorch、TensorFlowをcondaでインストールする方法は、それ 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (Berkeley から) Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer から公開された研究論文: [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) 1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** (from Stability AI) released with the paper [StableLM 3B 4E1T (Technical Report)](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu. 1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (from BigCode team) released with the paper [StarCoder 2 and The Stack v2: The Next Generation](https://arxiv.org/abs/2402.19173) by Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, Tianyang Liu, Max Tian, Denis Kocetkov, Arthur Zucker, Younes Belkada, Zijian Wang, Qian Liu, Dmitry Abulkhanov, Indraneil Paul, Zhuang Li, Wen-Ding Li, Megan Risdal, Jia Li, Jian Zhu, Terry Yue Zhuo, Evgenii Zheltonozhskii, Nii Osae Osae Dade, Wenhao Yu, Lucas Krauß, Naman Jain, Yixuan Su, Xuanli He, Manan Dey, Edoardo Abati, Yekun Chai, Niklas Muennighoff, Xiangru Tang, Muhtasham Oblokulov, Christopher Akiki, Marc Marone, Chenghao Mou, Mayank Mishra, Alex Gu, Binyuan Hui, Tri Dao, Armel Zebaze, Olivier Dehaene, Nicolas Patry, Canwen Xu, Julian McAuley, Han Hu, Torsten Scholak, Sebastien Paquet, Jennifer Robinson, Carolyn Jane Anderson, Nicolas Chapados, Mostofa Patwary, Nima Tajbakhsh, Yacine Jernite, Carlos Muñoz Ferrandis, Lingming Zhang, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries. +1. **[SuperPoint](https://huggingface.co/docs/transformers/main/model_doc/superpoint)** (from MagicLeap) released with the paper [SuperPoint: Self-Supervised Interest Point Detection and Description](https://arxiv.org/abs/1712.07629) by Daniel DeTone, Tomasz Malisiewicz and Andrew Rabinovich. 1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (MBZUAI から) Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan. から公開された研究論文 [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (Microsoft から) Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo から公開された研究論文: [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) 1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (Microsoft から) Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo から公開された研究論文: [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) diff --git a/README_ko.md b/README_ko.md index 9004123d880..0146c4d0048 100644 --- a/README_ko.md +++ b/README_ko.md @@ -420,6 +420,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (Berkeley 에서) Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer 의 [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) 논문과 함께 발표했습니다. 1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** (from Stability AI) released with the paper [StableLM 3B 4E1T (Technical Report)](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu. 1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (from BigCode team) released with the paper [StarCoder 2 and The Stack v2: The Next Generation](https://arxiv.org/abs/2402.19173) by Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, Tianyang Liu, Max Tian, Denis Kocetkov, Arthur Zucker, Younes Belkada, Zijian Wang, Qian Liu, Dmitry Abulkhanov, Indraneil Paul, Zhuang Li, Wen-Ding Li, Megan Risdal, Jia Li, Jian Zhu, Terry Yue Zhuo, Evgenii Zheltonozhskii, Nii Osae Osae Dade, Wenhao Yu, Lucas Krauß, Naman Jain, Yixuan Su, Xuanli He, Manan Dey, Edoardo Abati, Yekun Chai, Niklas Muennighoff, Xiangru Tang, Muhtasham Oblokulov, Christopher Akiki, Marc Marone, Chenghao Mou, Mayank Mishra, Alex Gu, Binyuan Hui, Tri Dao, Armel Zebaze, Olivier Dehaene, Nicolas Patry, Canwen Xu, Julian McAuley, Han Hu, Torsten Scholak, Sebastien Paquet, Jennifer Robinson, Carolyn Jane Anderson, Nicolas Chapados, Mostofa Patwary, Nima Tajbakhsh, Yacine Jernite, Carlos Muñoz Ferrandis, Lingming Zhang, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries. +1. **[SuperPoint](https://huggingface.co/docs/transformers/main/model_doc/superpoint)** (from MagicLeap) released with the paper [SuperPoint: Self-Supervised Interest Point Detection and Description](https://arxiv.org/abs/1712.07629) by Daniel DeTone, Tomasz Malisiewicz and Andrew Rabinovich. 1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (MBZUAI 에서 제공)은 Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.의 [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446)논문과 함께 발표했습니다. 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (Microsoft 에서) Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo 의 [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) 논문과 함께 발표했습니다. 1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (Microsoft 에서) Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo 의 [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) 논문과 함께 발표했습니다. diff --git a/README_pt-br.md b/README_pt-br.md index ef4c9b201b5..46b57278739 100644 --- a/README_pt-br.md +++ b/README_pt-br.md @@ -503,6 +503,7 @@ Número atual de pontos de verificação: ![](https://img.shields.io/endpoint?ur 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer. 1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** (from Stability AI) released with the paper [StableLM 3B 4E1T (Technical Report) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu. 1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (from BigCode team) released with the paper [StarCoder 2 and The Stack v2: The Next Generation](https://arxiv.org/abs/2402.19173) by Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, Tianyang Liu, Max Tian, Denis Kocetkov, Arthur Zucker, Younes Belkada, Zijian Wang, Qian Liu, Dmitry Abulkhanov, Indraneil Paul, Zhuang Li, Wen-Ding Li, Megan Risdal, Jia Li, Jian Zhu, Terry Yue Zhuo, Evgenii Zheltonozhskii, Nii Osae Osae Dade, Wenhao Yu, Lucas Krauß, Naman Jain, Yixuan Su, Xuanli He, Manan Dey, Edoardo Abati, Yekun Chai, Niklas Muennighoff, Xiangru Tang, Muhtasham Oblokulov, Christopher Akiki, Marc Marone, Chenghao Mou, Mayank Mishra, Alex Gu, Binyuan Hui, Tri Dao, Armel Zebaze, Olivier Dehaene, Nicolas Patry, Canwen Xu, Julian McAuley, Han Hu, Torsten Scholak, Sebastien Paquet, Jennifer Robinson, Carolyn Jane Anderson, Nicolas Chapados, Mostofa Patwary, Nima Tajbakhsh, Yacine Jernite, Carlos Muñoz Ferrandis, Lingming Zhang, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries. +1. **[SuperPoint](https://huggingface.co/docs/transformers/main/model_doc/superpoint)** (from MagicLeap) released with the paper [SuperPoint: Self-Supervised Interest Point Detection and Description](https://arxiv.org/abs/1712.07629) by Daniel DeTone, Tomasz Malisiewicz and Andrew Rabinovich. 1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (from MBZUAI) released with the paper [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan. 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo. 1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo. diff --git a/README_ru.md b/README_ru.md index 8139fa51b99..aecd7bde1ea 100644 --- a/README_ru.md +++ b/README_ru.md @@ -493,6 +493,7 @@ conda install conda-forge::transformers 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer. 1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** (from Stability AI) released with the paper [StableLM 3B 4E1T (Technical Report) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu. 1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (from BigCode team) released with the paper [StarCoder 2 and The Stack v2: The Next Generation](https://arxiv.org/abs/2402.19173) by Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, Tianyang Liu, Max Tian, Denis Kocetkov, Arthur Zucker, Younes Belkada, Zijian Wang, Qian Liu, Dmitry Abulkhanov, Indraneil Paul, Zhuang Li, Wen-Ding Li, Megan Risdal, Jia Li, Jian Zhu, Terry Yue Zhuo, Evgenii Zheltonozhskii, Nii Osae Osae Dade, Wenhao Yu, Lucas Krauß, Naman Jain, Yixuan Su, Xuanli He, Manan Dey, Edoardo Abati, Yekun Chai, Niklas Muennighoff, Xiangru Tang, Muhtasham Oblokulov, Christopher Akiki, Marc Marone, Chenghao Mou, Mayank Mishra, Alex Gu, Binyuan Hui, Tri Dao, Armel Zebaze, Olivier Dehaene, Nicolas Patry, Canwen Xu, Julian McAuley, Han Hu, Torsten Scholak, Sebastien Paquet, Jennifer Robinson, Carolyn Jane Anderson, Nicolas Chapados, Mostofa Patwary, Nima Tajbakhsh, Yacine Jernite, Carlos Muñoz Ferrandis, Lingming Zhang, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries. +1. **[SuperPoint](https://huggingface.co/docs/transformers/main/model_doc/superpoint)** (from MagicLeap) released with the paper [SuperPoint: Self-Supervised Interest Point Detection and Description](https://arxiv.org/abs/1712.07629) by Daniel DeTone, Tomasz Malisiewicz and Andrew Rabinovich. 1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (from MBZUAI) released with the paper [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan. 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo. 1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo. diff --git a/README_te.md b/README_te.md index 00acdccd28c..a59093b691b 100644 --- a/README_te.md +++ b/README_te.md @@ -495,6 +495,7 @@ Flax, PyTorch లేదా TensorFlow యొక్క ఇన్‌స్టా 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer. 1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** (from Stability AI) released with the paper [StableLM 3B 4E1T (Technical Report) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu. 1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (from BigCode team) released with the paper [StarCoder 2 and The Stack v2: The Next Generation](https://arxiv.org/abs/2402.19173) by Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, Tianyang Liu, Max Tian, Denis Kocetkov, Arthur Zucker, Younes Belkada, Zijian Wang, Qian Liu, Dmitry Abulkhanov, Indraneil Paul, Zhuang Li, Wen-Ding Li, Megan Risdal, Jia Li, Jian Zhu, Terry Yue Zhuo, Evgenii Zheltonozhskii, Nii Osae Osae Dade, Wenhao Yu, Lucas Krauß, Naman Jain, Yixuan Su, Xuanli He, Manan Dey, Edoardo Abati, Yekun Chai, Niklas Muennighoff, Xiangru Tang, Muhtasham Oblokulov, Christopher Akiki, Marc Marone, Chenghao Mou, Mayank Mishra, Alex Gu, Binyuan Hui, Tri Dao, Armel Zebaze, Olivier Dehaene, Nicolas Patry, Canwen Xu, Julian McAuley, Han Hu, Torsten Scholak, Sebastien Paquet, Jennifer Robinson, Carolyn Jane Anderson, Nicolas Chapados, Mostofa Patwary, Nima Tajbakhsh, Yacine Jernite, Carlos Muñoz Ferrandis, Lingming Zhang, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries. +1. **[SuperPoint](https://huggingface.co/docs/transformers/main/model_doc/superpoint)** (from MagicLeap) released with the paper [SuperPoint: Self-Supervised Interest Point Detection and Description](https://arxiv.org/abs/1712.07629) by Daniel DeTone, Tomasz Malisiewicz and Andrew Rabinovich. 1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (from MBZUAI) released with the paper [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan. 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo. 1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo. diff --git a/README_vi.md b/README_vi.md index 3a0a2f09db0..7edada40047 100644 --- a/README_vi.md +++ b/README_vi.md @@ -494,6 +494,7 @@ Số lượng điểm kiểm tra hiện tại: ![](https://img.shields.io/endpoi 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (từ Berkeley) được phát hành với bài báo [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer. 1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** (từ Stability AI) được phát hành với bài báo [StableLM 3B 4E1T (Technical Report)](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu. 1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (từ BigCode team) được phát hành với bài báo [StarCoder 2 and The Stack v2: The Next Generation](https://arxiv.org/abs/2402.19173) by Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, Tianyang Liu, Max Tian, Denis Kocetkov, Arthur Zucker, Younes Belkada, Zijian Wang, Qian Liu, Dmitry Abulkhanov, Indraneil Paul, Zhuang Li, Wen-Ding Li, Megan Risdal, Jia Li, Jian Zhu, Terry Yue Zhuo, Evgenii Zheltonozhskii, Nii Osae Osae Dade, Wenhao Yu, Lucas Krauß, Naman Jain, Yixuan Su, Xuanli He, Manan Dey, Edoardo Abati, Yekun Chai, Niklas Muennighoff, Xiangru Tang, Muhtasham Oblokulov, Christopher Akiki, Marc Marone, Chenghao Mou, Mayank Mishra, Alex Gu, Binyuan Hui, Tri Dao, Armel Zebaze, Olivier Dehaene, Nicolas Patry, Canwen Xu, Julian McAuley, Han Hu, Torsten Scholak, Sebastien Paquet, Jennifer Robinson, Carolyn Jane Anderson, Nicolas Chapados, Mostofa Patwary, Nima Tajbakhsh, Yacine Jernite, Carlos Muñoz Ferrandis, Lingming Zhang, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries. +1. **[SuperPoint](https://huggingface.co/docs/transformers/main/model_doc/superpoint)** (từ MagicLeap) được phát hành với bài báo [SuperPoint: Self-Supervised Interest Point Detection and Description](https://arxiv.org/abs/1712.07629) by Daniel DeTone, Tomasz Malisiewicz and Andrew Rabinovich. 1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (từ MBZUAI) được phát hành với bài báo [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan. 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (từ Microsoft) được phát hành với bài báo [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo. 1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (từ Microsoft) được phát hành với bài báo [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo. diff --git a/README_zh-hans.md b/README_zh-hans.md index 52a244bd8aa..1ae70393712 100644 --- a/README_zh-hans.md +++ b/README_zh-hans.md @@ -444,6 +444,7 @@ conda install conda-forge::transformers 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (来自 Berkeley) 伴随论文 [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) 由 Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer 发布。 1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** (from Stability AI) released with the paper [StableLM 3B 4E1T (Technical Report)](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu. 1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (from BigCode team) released with the paper [StarCoder 2 and The Stack v2: The Next Generation](https://arxiv.org/abs/2402.19173) by Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, Tianyang Liu, Max Tian, Denis Kocetkov, Arthur Zucker, Younes Belkada, Zijian Wang, Qian Liu, Dmitry Abulkhanov, Indraneil Paul, Zhuang Li, Wen-Ding Li, Megan Risdal, Jia Li, Jian Zhu, Terry Yue Zhuo, Evgenii Zheltonozhskii, Nii Osae Osae Dade, Wenhao Yu, Lucas Krauß, Naman Jain, Yixuan Su, Xuanli He, Manan Dey, Edoardo Abati, Yekun Chai, Niklas Muennighoff, Xiangru Tang, Muhtasham Oblokulov, Christopher Akiki, Marc Marone, Chenghao Mou, Mayank Mishra, Alex Gu, Binyuan Hui, Tri Dao, Armel Zebaze, Olivier Dehaene, Nicolas Patry, Canwen Xu, Julian McAuley, Han Hu, Torsten Scholak, Sebastien Paquet, Jennifer Robinson, Carolyn Jane Anderson, Nicolas Chapados, Mostofa Patwary, Nima Tajbakhsh, Yacine Jernite, Carlos Muñoz Ferrandis, Lingming Zhang, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries. +1. **[SuperPoint](https://huggingface.co/docs/transformers/main/model_doc/superpoint)** (from MagicLeap) released with the paper [SuperPoint: Self-Supervised Interest Point Detection and Description](https://arxiv.org/abs/1712.07629) by Daniel DeTone, Tomasz Malisiewicz and Andrew Rabinovich. 1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (来自 MBZUAI) 伴随论文 [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) 由 Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan 发布。 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (来自 Microsoft) 伴随论文 [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) 由 Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo 发布。 1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (来自 Microsoft) 伴随论文 [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) 由 Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo 发布。 diff --git a/README_zh-hant.md b/README_zh-hant.md index fd306c6176f..f936fa897e6 100644 --- a/README_zh-hant.md +++ b/README_zh-hant.md @@ -456,6 +456,7 @@ conda install conda-forge::transformers 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer. 1. **[StableLm](https://huggingface.co/docs/transformers/model_doc/stablelm)** released with the paper [StableLM 3B 4E1T (Technical Report)](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Jonathan Tow, Marco Bellagente, Dakota Mahan, Carlos Riquelme Ruiz, Duy Phung, Maksym Zhuravinskyi, Nathan Cooper, Nikhil Pinnaparaju, Reshinth Adithyan, and James Baicoianu. 1. **[Starcoder2](https://huggingface.co/docs/transformers/main/model_doc/starcoder2)** (from BigCode team) released with the paper [StarCoder 2 and The Stack v2: The Next Generation](https://arxiv.org/abs/2402.19173) by Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, Tianyang Liu, Max Tian, Denis Kocetkov, Arthur Zucker, Younes Belkada, Zijian Wang, Qian Liu, Dmitry Abulkhanov, Indraneil Paul, Zhuang Li, Wen-Ding Li, Megan Risdal, Jia Li, Jian Zhu, Terry Yue Zhuo, Evgenii Zheltonozhskii, Nii Osae Osae Dade, Wenhao Yu, Lucas Krauß, Naman Jain, Yixuan Su, Xuanli He, Manan Dey, Edoardo Abati, Yekun Chai, Niklas Muennighoff, Xiangru Tang, Muhtasham Oblokulov, Christopher Akiki, Marc Marone, Chenghao Mou, Mayank Mishra, Alex Gu, Binyuan Hui, Tri Dao, Armel Zebaze, Olivier Dehaene, Nicolas Patry, Canwen Xu, Julian McAuley, Han Hu, Torsten Scholak, Sebastien Paquet, Jennifer Robinson, Carolyn Jane Anderson, Nicolas Chapados, Mostofa Patwary, Nima Tajbakhsh, Yacine Jernite, Carlos Muñoz Ferrandis, Lingming Zhang, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries. +1. **[SuperPoint](https://huggingface.co/docs/transformers/main/model_doc/superpoint)** (from MagicLeap) released with the paper [SuperPoint: Self-Supervised Interest Point Detection and Description](https://arxiv.org/abs/1712.07629) by Daniel DeTone, Tomasz Malisiewicz and Andrew Rabinovich. 1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (from MBZUAI) released with the paper [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan. 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo. 1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo. diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml index 47c7ff6602c..db6c6e0c6fa 100644 --- a/docs/source/en/_toctree.yml +++ b/docs/source/en/_toctree.yml @@ -595,6 +595,8 @@ title: SegFormer - local: model_doc/seggpt title: SegGpt + - local: model_doc/superpoint + title: SuperPoint - local: model_doc/swiftformer title: SwiftFormer - local: model_doc/swin diff --git a/docs/source/en/index.md b/docs/source/en/index.md index ec8def2b2ef..b145fb8660a 100644 --- a/docs/source/en/index.md +++ b/docs/source/en/index.md @@ -266,6 +266,7 @@ Flax), PyTorch, and/or TensorFlow. | [SqueezeBERT](model_doc/squeezebert) | ✅ | ❌ | ❌ | | [StableLm](model_doc/stablelm) | ✅ | ❌ | ❌ | | [Starcoder2](model_doc/starcoder2) | ✅ | ❌ | ❌ | +| [SuperPoint](model_doc/superpoint) | ✅ | ❌ | ❌ | | [SwiftFormer](model_doc/swiftformer) | ✅ | ❌ | ❌ | | [Swin Transformer](model_doc/swin) | ✅ | ✅ | ❌ | | [Swin Transformer V2](model_doc/swinv2) | ✅ | ❌ | ❌ | diff --git a/docs/source/en/model_doc/superpoint.md b/docs/source/en/model_doc/superpoint.md new file mode 100644 index 00000000000..b3f9f8c9201 --- /dev/null +++ b/docs/source/en/model_doc/superpoint.md @@ -0,0 +1,122 @@ + + +# SuperPoint + +## Overview + +The SuperPoint model was proposed +in [SuperPoint: Self-Supervised Interest Point Detection and Description](https://arxiv.org/abs/1712.07629) by Daniel +DeTone, Tomasz Malisiewicz and Andrew Rabinovich. + +This model is the result of a self-supervised training of a fully-convolutional network for interest point detection and +description. The model is able to detect interest points that are repeatable under homographic transformations and +provide a descriptor for each point. The use of the model in its own is limited, but it can be used as a feature +extractor for other tasks such as homography estimation, image matching, etc. + +The abstract from the paper is the following: + +*This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a +large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our +fully-convolutional model operates on full-sized images and jointly computes pixel-level interest point locations and +associated descriptors in one forward pass. We introduce Homographic Adaptation, a multi-scale, multi-homography +approach for boosting interest point detection repeatability and performing cross-domain adaptation (e.g., +synthetic-to-real). Our model, when trained on the MS-COCO generic image dataset using Homographic Adaptation, is able +to repeatedly detect a much richer set of interest points than the initial pre-adapted deep model and any other +traditional corner detector. The final system gives rise to state-of-the-art homography estimation results on HPatches +when compared to LIFT, SIFT and ORB.* + +## How to use + +Here is a quick example of using the model to detect interest points in an image: + +```python +from transformers import AutoImageProcessor, AutoModel +import torch +from PIL import Image +import requests + +url = "http://images.cocodataset.org/val2017/000000039769.jpg" +image = Image.open(requests.get(url, stream=True).raw) + +processor = AutoImageProcessor.from_pretrained("magic-leap-community/superpoint") +model = AutoModel.from_pretrained("magic-leap-community/superpoint") + +inputs = processor(image, return_tensors="pt") +outputs = model(**inputs) +``` + +The outputs contain the list of keypoint coordinates with their respective score and description (a 256-long vector). + +You can also feed multiple images to the model. Due to the nature of SuperPoint, to output a dynamic number of keypoints, +you will need to use the mask attribute to retrieve the respective information : + +```python +from transformers import AutoImageProcessor, AutoModel +import torch +from PIL import Image +import requests + +url_image_1 = "http://images.cocodataset.org/val2017/000000039769.jpg" +image_1 = Image.open(requests.get(url_image_1, stream=True).raw) +url_image_2 = "http://images.cocodataset.org/test-stuff2017/000000000568.jpg" +image_2 = Image.open(requests.get(url_image_2, stream=True).raw) + +images = [image_1, image_2] + +processor = AutoImageProcessor.from_pretrained("magic-leap-community/superpoint") +model = AutoModel.from_pretrained("magic-leap-community/superpoint") + +inputs = processor(images, return_tensors="pt") +outputs = model(**inputs) + +for i in range(len(images)): + image_mask = outputs.mask[i] + image_indices = torch.nonzero(image_mask).squeeze() + image_keypoints = outputs.keypoints[i][image_indices] + image_scores = outputs.scores[i][image_indices] + image_descriptors = outputs.descriptors[i][image_indices] +``` + +You can then print the keypoints on the image to visualize the result : +```python +import cv2 +for keypoint, score in zip(image_keypoints, image_scores): + keypoint_x, keypoint_y = int(keypoint[0].item()), int(keypoint[1].item()) + color = tuple([score.item() * 255] * 3) + image = cv2.circle(image, (keypoint_x, keypoint_y), 2, color) +cv2.imwrite("output_image.png", image) +``` + +This model was contributed by [stevenbucaille](https://huggingface.co/stevenbucaille). +The original code can be found [here](https://github.com/magicleap/SuperPointPretrainedNetwork). + +## SuperPointConfig + +[[autodoc]] SuperPointConfig + +## SuperPointImageProcessor + +[[autodoc]] SuperPointImageProcessor + +- preprocess + +## SuperPointModel + +[[autodoc]] SuperPointModel + +- forward + + diff --git a/src/transformers/__init__.py b/src/transformers/__init__.py index 631276d3c19..7c3b34ce6e5 100644 --- a/src/transformers/__init__.py +++ b/src/transformers/__init__.py @@ -819,6 +819,7 @@ _import_structure = { ], "models.stablelm": ["STABLELM_PRETRAINED_CONFIG_ARCHIVE_MAP", "StableLmConfig"], "models.starcoder2": ["STARCODER2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Starcoder2Config"], + "models.superpoint": ["SUPERPOINT_PRETRAINED_CONFIG_ARCHIVE_MAP", "SuperPointConfig"], "models.swiftformer": [ "SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwiftFormerConfig", @@ -1339,6 +1340,7 @@ else: _import_structure["models.segformer"].extend(["SegformerFeatureExtractor", "SegformerImageProcessor"]) _import_structure["models.seggpt"].extend(["SegGptImageProcessor"]) _import_structure["models.siglip"].append("SiglipImageProcessor") + _import_structure["models.superpoint"].extend(["SuperPointImageProcessor"]) _import_structure["models.swin2sr"].append("Swin2SRImageProcessor") _import_structure["models.tvlt"].append("TvltImageProcessor") _import_structure["models.tvp"].append("TvpImageProcessor") @@ -3336,6 +3338,13 @@ else: "Starcoder2PreTrainedModel", ] ) + _import_structure["models.superpoint"].extend( + [ + "SUPERPOINT_PRETRAINED_MODEL_ARCHIVE_LIST", + "SuperPointModel", + "SuperPointPreTrainedModel", + ] + ) _import_structure["models.swiftformer"].extend( [ "SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", @@ -5670,6 +5679,7 @@ if TYPE_CHECKING: ) from .models.stablelm import STABLELM_PRETRAINED_CONFIG_ARCHIVE_MAP, StableLmConfig from .models.starcoder2 import STARCODER2_PRETRAINED_CONFIG_ARCHIVE_MAP, Starcoder2Config + from .models.superpoint import SUPERPOINT_PRETRAINED_CONFIG_ARCHIVE_MAP, SuperPointConfig from .models.swiftformer import ( SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SwiftFormerConfig, @@ -6182,6 +6192,7 @@ if TYPE_CHECKING: from .models.segformer import SegformerFeatureExtractor, SegformerImageProcessor from .models.seggpt import SegGptImageProcessor from .models.siglip import SiglipImageProcessor + from .models.superpoint import SuperPointImageProcessor from .models.swin2sr import Swin2SRImageProcessor from .models.tvlt import TvltImageProcessor from .models.tvp import TvpImageProcessor @@ -7839,6 +7850,11 @@ if TYPE_CHECKING: Starcoder2Model, Starcoder2PreTrainedModel, ) + from .models.superpoint import ( + SUPERPOINT_PRETRAINED_MODEL_ARCHIVE_LIST, + SuperPointModel, + SuperPointPreTrainedModel, + ) from .models.swiftformer import ( SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, SwiftFormerForImageClassification, diff --git a/src/transformers/models/__init__.py b/src/transformers/models/__init__.py index d0f3ba2688e..f6dc3e2014a 100644 --- a/src/transformers/models/__init__.py +++ b/src/transformers/models/__init__.py @@ -210,6 +210,7 @@ from . import ( squeezebert, stablelm, starcoder2, + superpoint, swiftformer, swin, swin2sr, diff --git a/src/transformers/models/auto/__init__.py b/src/transformers/models/auto/__init__.py index 3db995a9c74..3d8a35b11fd 100644 --- a/src/transformers/models/auto/__init__.py +++ b/src/transformers/models/auto/__init__.py @@ -117,6 +117,7 @@ else: "AutoModelWithLMHead", "AutoModelForZeroShotImageClassification", "AutoModelForZeroShotObjectDetection", + "AutoModelForKeypointDetection", ] try: @@ -278,6 +279,7 @@ if TYPE_CHECKING: AutoModelForImageSegmentation, AutoModelForImageToImage, AutoModelForInstanceSegmentation, + AutoModelForKeypointDetection, AutoModelForMaskedImageModeling, AutoModelForMaskedLM, AutoModelForMaskGeneration, diff --git a/src/transformers/models/auto/configuration_auto.py b/src/transformers/models/auto/configuration_auto.py index cd5be302c42..3d98ef464d9 100755 --- a/src/transformers/models/auto/configuration_auto.py +++ b/src/transformers/models/auto/configuration_auto.py @@ -219,6 +219,7 @@ CONFIG_MAPPING_NAMES = OrderedDict( ("squeezebert", "SqueezeBertConfig"), ("stablelm", "StableLmConfig"), ("starcoder2", "Starcoder2Config"), + ("superpoint", "SuperPointConfig"), ("swiftformer", "SwiftFormerConfig"), ("swin", "SwinConfig"), ("swin2sr", "Swin2SRConfig"), @@ -450,6 +451,7 @@ CONFIG_ARCHIVE_MAP_MAPPING_NAMES = OrderedDict( ("squeezebert", "SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"), ("stablelm", "STABLELM_PRETRAINED_CONFIG_ARCHIVE_MAP"), ("starcoder2", "STARCODER2_PRETRAINED_CONFIG_ARCHIVE_MAP"), + ("superpoint", "SUPERPOINT_PRETRAINED_CONFIG_ARCHIVE_MAP"), ("swiftformer", "SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"), ("swin", "SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP"), ("swin2sr", "SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP"), @@ -711,6 +713,7 @@ MODEL_NAMES_MAPPING = OrderedDict( ("squeezebert", "SqueezeBERT"), ("stablelm", "StableLm"), ("starcoder2", "Starcoder2"), + ("superpoint", "SuperPoint"), ("swiftformer", "SwiftFormer"), ("swin", "Swin Transformer"), ("swin2sr", "Swin2SR"), diff --git a/src/transformers/models/auto/modeling_auto.py b/src/transformers/models/auto/modeling_auto.py index d2cb6882cd3..3db43d67b9a 100755 --- a/src/transformers/models/auto/modeling_auto.py +++ b/src/transformers/models/auto/modeling_auto.py @@ -207,6 +207,7 @@ MODEL_MAPPING_NAMES = OrderedDict( ("squeezebert", "SqueezeBertModel"), ("stablelm", "StableLmModel"), ("starcoder2", "Starcoder2Model"), + ("superpoint", "SuperPointModel"), ("swiftformer", "SwiftFormerModel"), ("swin", "SwinModel"), ("swin2sr", "Swin2SRModel"), diff --git a/src/transformers/models/superpoint/__init__.py b/src/transformers/models/superpoint/__init__.py new file mode 100644 index 00000000000..610045bb71f --- /dev/null +++ b/src/transformers/models/superpoint/__init__.py @@ -0,0 +1,77 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +# rely on isort to merge the imports +from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available + + +_import_structure = { + "configuration_superpoint": [ + "SUPERPOINT_PRETRAINED_CONFIG_ARCHIVE_MAP", + "SuperPointConfig", + ] +} + +try: + if not is_vision_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["image_processing_superpoint"] = ["SuperPointImageProcessor"] + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_superpoint"] = [ + "SUPERPOINT_PRETRAINED_MODEL_ARCHIVE_LIST", + "SuperPointModel", + "SuperPointPreTrainedModel", + ] + + +if TYPE_CHECKING: + from .configuration_superpoint import ( + SUPERPOINT_PRETRAINED_CONFIG_ARCHIVE_MAP, + SuperPointConfig, + ) + + try: + if not is_vision_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .image_processing_superpoint import SuperPointImageProcessor + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_superpoint import ( + SUPERPOINT_PRETRAINED_MODEL_ARCHIVE_LIST, + SuperPointModel, + SuperPointPreTrainedModel, + ) + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure) diff --git a/src/transformers/models/superpoint/configuration_superpoint.py b/src/transformers/models/superpoint/configuration_superpoint.py new file mode 100644 index 00000000000..4a72c391c83 --- /dev/null +++ b/src/transformers/models/superpoint/configuration_superpoint.py @@ -0,0 +1,91 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import List + +from ...configuration_utils import PretrainedConfig +from ...utils import logging + + +logger = logging.get_logger(__name__) + +SUPERPOINT_PRETRAINED_CONFIG_ARCHIVE_MAP = { + "magic-leap-community/superpoint": "https://huggingface.co/magic-leap-community/superpoint/blob/main/config.json" +} + + +class SuperPointConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`SuperPointModel`]. It is used to instantiate a + SuperPoint model according to the specified arguments, defining the model architecture. Instantiating a + configuration with the defaults will yield a similar configuration to that of the SuperPoint + [magic-leap-community/superpoint](https://huggingface.co/magic-leap-community/superpoint) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + encoder_hidden_sizes (`List`, *optional*, defaults to `[64, 64, 128, 128]`): + The number of channels in each convolutional layer in the encoder. + decoder_hidden_size (`int`, *optional*, defaults to 256): The hidden size of the decoder. + keypoint_decoder_dim (`int`, *optional*, defaults to 65): The output dimension of the keypoint decoder. + descriptor_decoder_dim (`int`, *optional*, defaults to 256): The output dimension of the descriptor decoder. + keypoint_threshold (`float`, *optional*, defaults to 0.005): + The threshold to use for extracting keypoints. + max_keypoints (`int`, *optional*, defaults to -1): + The maximum number of keypoints to extract. If `-1`, will extract all keypoints. + nms_radius (`int`, *optional*, defaults to 4): + The radius for non-maximum suppression. + border_removal_distance (`int`, *optional*, defaults to 4): + The distance from the border to remove keypoints. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + + Example: + ```python + >>> from transformers import SuperPointConfig, SuperPointModel + + >>> # Initializing a SuperPoint superpoint style configuration + >>> configuration = SuperPointConfig() + >>> # Initializing a model from the superpoint style configuration + >>> model = SuperPointModel(configuration) + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "superpoint" + + def __init__( + self, + encoder_hidden_sizes: List[int] = [64, 64, 128, 128], + decoder_hidden_size: int = 256, + keypoint_decoder_dim: int = 65, + descriptor_decoder_dim: int = 256, + keypoint_threshold: float = 0.005, + max_keypoints: int = -1, + nms_radius: int = 4, + border_removal_distance: int = 4, + initializer_range=0.02, + **kwargs, + ): + self.encoder_hidden_sizes = encoder_hidden_sizes + self.decoder_hidden_size = decoder_hidden_size + self.keypoint_decoder_dim = keypoint_decoder_dim + self.descriptor_decoder_dim = descriptor_decoder_dim + self.keypoint_threshold = keypoint_threshold + self.max_keypoints = max_keypoints + self.nms_radius = nms_radius + self.border_removal_distance = border_removal_distance + self.initializer_range = initializer_range + + super().__init__(**kwargs) diff --git a/src/transformers/models/superpoint/convert_superpoint_to_pytorch.py b/src/transformers/models/superpoint/convert_superpoint_to_pytorch.py new file mode 100644 index 00000000000..2bad5845355 --- /dev/null +++ b/src/transformers/models/superpoint/convert_superpoint_to_pytorch.py @@ -0,0 +1,175 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import argparse +import os + +import requests +import torch +from PIL import Image + +from transformers import SuperPointConfig, SuperPointImageProcessor, SuperPointModel + + +def get_superpoint_config(): + config = SuperPointConfig( + encoder_hidden_sizes=[64, 64, 128, 128], + decoder_hidden_size=256, + keypoint_decoder_dim=65, + descriptor_decoder_dim=256, + keypoint_threshold=0.005, + max_keypoints=-1, + nms_radius=4, + border_removal_distance=4, + initializer_range=0.02, + ) + + return config + + +def create_rename_keys(config, state_dict): + rename_keys = [] + + # Encoder weights + rename_keys.append(("conv1a.weight", "encoder.conv_blocks.0.conv_a.weight")) + rename_keys.append(("conv1b.weight", "encoder.conv_blocks.0.conv_b.weight")) + rename_keys.append(("conv2a.weight", "encoder.conv_blocks.1.conv_a.weight")) + rename_keys.append(("conv2b.weight", "encoder.conv_blocks.1.conv_b.weight")) + rename_keys.append(("conv3a.weight", "encoder.conv_blocks.2.conv_a.weight")) + rename_keys.append(("conv3b.weight", "encoder.conv_blocks.2.conv_b.weight")) + rename_keys.append(("conv4a.weight", "encoder.conv_blocks.3.conv_a.weight")) + rename_keys.append(("conv4b.weight", "encoder.conv_blocks.3.conv_b.weight")) + rename_keys.append(("conv1a.bias", "encoder.conv_blocks.0.conv_a.bias")) + rename_keys.append(("conv1b.bias", "encoder.conv_blocks.0.conv_b.bias")) + rename_keys.append(("conv2a.bias", "encoder.conv_blocks.1.conv_a.bias")) + rename_keys.append(("conv2b.bias", "encoder.conv_blocks.1.conv_b.bias")) + rename_keys.append(("conv3a.bias", "encoder.conv_blocks.2.conv_a.bias")) + rename_keys.append(("conv3b.bias", "encoder.conv_blocks.2.conv_b.bias")) + rename_keys.append(("conv4a.bias", "encoder.conv_blocks.3.conv_a.bias")) + rename_keys.append(("conv4b.bias", "encoder.conv_blocks.3.conv_b.bias")) + + # Keypoint Decoder weights + rename_keys.append(("convPa.weight", "keypoint_decoder.conv_score_a.weight")) + rename_keys.append(("convPb.weight", "keypoint_decoder.conv_score_b.weight")) + rename_keys.append(("convPa.bias", "keypoint_decoder.conv_score_a.bias")) + rename_keys.append(("convPb.bias", "keypoint_decoder.conv_score_b.bias")) + + # Descriptor Decoder weights + rename_keys.append(("convDa.weight", "descriptor_decoder.conv_descriptor_a.weight")) + rename_keys.append(("convDb.weight", "descriptor_decoder.conv_descriptor_b.weight")) + rename_keys.append(("convDa.bias", "descriptor_decoder.conv_descriptor_a.bias")) + rename_keys.append(("convDb.bias", "descriptor_decoder.conv_descriptor_b.bias")) + + return rename_keys + + +def rename_key(dct, old, new): + val = dct.pop(old) + dct[new] = val + + +def prepare_imgs(): + url = "http://images.cocodataset.org/val2017/000000039769.jpg" + im1 = Image.open(requests.get(url, stream=True).raw) + url = "http://images.cocodataset.org/test-stuff2017/000000004016.jpg" + im2 = Image.open(requests.get(url, stream=True).raw) + return [im1, im2] + + +@torch.no_grad() +def convert_superpoint_checkpoint(checkpoint_url, pytorch_dump_folder_path, save_model, push_to_hub, test_mode=False): + """ + Copy/paste/tweak model's weights to our SuperPoint structure. + """ + + print("Downloading original model from checkpoint...") + config = get_superpoint_config() + + # load original state_dict from URL + original_state_dict = torch.hub.load_state_dict_from_url(checkpoint_url) + + print("Converting model parameters...") + # rename keys + rename_keys = create_rename_keys(config, original_state_dict) + new_state_dict = original_state_dict.copy() + for src, dest in rename_keys: + rename_key(new_state_dict, src, dest) + + # Load HuggingFace model + model = SuperPointModel(config) + model.load_state_dict(new_state_dict) + model.eval() + print("Successfully loaded weights in the model") + + # Check model outputs + preprocessor = SuperPointImageProcessor() + inputs = preprocessor(images=prepare_imgs(), return_tensors="pt") + outputs = model(**inputs) + + # If test_mode is True, we check that the model outputs match the original results + if test_mode: + torch.count_nonzero(outputs.mask[0]) + expected_keypoints_shape = (2, 830, 2) + expected_scores_shape = (2, 830) + expected_descriptors_shape = (2, 830, 256) + + expected_keypoints_values = torch.tensor([[480.0, 9.0], [494.0, 9.0], [489.0, 16.0]]) + expected_scores_values = torch.tensor([0.0064, 0.0140, 0.0595, 0.0728, 0.5170, 0.0175, 0.1523, 0.2055, 0.0336]) + expected_descriptors_value = torch.tensor(-0.1096) + assert outputs.keypoints.shape == expected_keypoints_shape + assert outputs.scores.shape == expected_scores_shape + assert outputs.descriptors.shape == expected_descriptors_shape + + assert torch.allclose(outputs.keypoints[0, :3], expected_keypoints_values, atol=1e-3) + assert torch.allclose(outputs.scores[0, :9], expected_scores_values, atol=1e-3) + assert torch.allclose(outputs.descriptors[0, 0, 0], expected_descriptors_value, atol=1e-3) + print("Model outputs match the original results!") + + if save_model: + print("Saving model to local...") + # Create folder to save model + if not os.path.isdir(pytorch_dump_folder_path): + os.mkdir(pytorch_dump_folder_path) + + model.save_pretrained(pytorch_dump_folder_path) + preprocessor.save_pretrained(pytorch_dump_folder_path) + + model_name = "superpoint" + if push_to_hub: + print(f"Pushing {model_name} to the hub...") + model.push_to_hub(model_name) + preprocessor.push_to_hub(model_name) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + # Required parameters + parser.add_argument( + "--checkpoint_url", + default="https://github.com/magicleap/SuperPointPretrainedNetwork/raw/master/superpoint_v1.pth", + type=str, + help="URL of the original SuperPoint checkpoint you'd like to convert.", + ) + parser.add_argument( + "--pytorch_dump_folder_path", + default="model", + type=str, + help="Path to the output PyTorch model directory.", + ) + parser.add_argument("--save_model", action="store_true", help="Save model to local") + parser.add_argument("--push_to_hub", action="store_true", help="Push model and image preprocessor to the hub") + + args = parser.parse_args() + convert_superpoint_checkpoint( + args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub + ) diff --git a/src/transformers/models/superpoint/image_processing_superpoint.py b/src/transformers/models/superpoint/image_processing_superpoint.py new file mode 100644 index 00000000000..8c7e2a7deba --- /dev/null +++ b/src/transformers/models/superpoint/image_processing_superpoint.py @@ -0,0 +1,272 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for SuperPoint.""" + +from typing import Dict, Optional, Union + +import numpy as np + +from ... import is_vision_available, requires_backends +from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict +from ...image_transforms import resize, to_channel_dimension_format +from ...image_utils import ( + ChannelDimension, + ImageInput, + infer_channel_dimension_format, + is_scaled_image, + make_list_of_images, + to_numpy_array, + valid_images, +) +from ...utils import TensorType, logging + + +if is_vision_available(): + import PIL + +logger = logging.get_logger(__name__) + + +def is_grayscale( + image: ImageInput, + input_data_format: Optional[Union[str, ChannelDimension]] = None, +): + if input_data_format == ChannelDimension.FIRST: + return np.all(image[0, ...] == image[1, ...]) and np.all(image[1, ...] == image[2, ...]) + elif input_data_format == ChannelDimension.LAST: + return np.all(image[..., 0] == image[..., 1]) and np.all(image[..., 1] == image[..., 2]) + + +def convert_to_grayscale( + image: ImageInput, + input_data_format: Optional[Union[str, ChannelDimension]] = None, +) -> ImageInput: + """ + Converts an image to grayscale format using the NTSC formula. Only support numpy and PIL Image. TODO support torch + and tensorflow grayscale conversion + + This function is supposed to return a 1-channel image, but it returns a 3-channel image with the same value in each + channel, because of an issue that is discussed in : + https://github.com/huggingface/transformers/pull/25786#issuecomment-1730176446 + + Args: + image (Image): + The image to convert. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format for the input image. + """ + requires_backends(convert_to_grayscale, ["vision"]) + + if isinstance(image, np.ndarray): + if input_data_format == ChannelDimension.FIRST: + gray_image = image[0, ...] * 0.2989 + image[1, ...] * 0.5870 + image[2, ...] * 0.1140 + gray_image = np.stack([gray_image] * 3, axis=0) + elif input_data_format == ChannelDimension.LAST: + gray_image = image[..., 0] * 0.2989 + image[..., 1] * 0.5870 + image[..., 2] * 0.1140 + gray_image = np.stack([gray_image] * 3, axis=-1) + return gray_image + + if not isinstance(image, PIL.Image.Image): + return image + + image = image.convert("L") + return image + + +class SuperPointImageProcessor(BaseImageProcessor): + r""" + Constructs a SuperPoint image processor. + + Args: + do_resize (`bool`, *optional*, defaults to `True`): + Controls whether to resize the image's (height, width) dimensions to the specified `size`. Can be overriden + by `do_resize` in the `preprocess` method. + size (`Dict[str, int]` *optional*, defaults to `{"height": 480, "width": 640}`): + Resolution of the output image after `resize` is applied. Only has an effect if `do_resize` is set to + `True`. Can be overriden by `size` in the `preprocess` method. + do_rescale (`bool`, *optional*, defaults to `True`): + Whether to rescale the image by the specified scale `rescale_factor`. Can be overriden by `do_rescale` in + the `preprocess` method. + rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): + Scale factor to use if rescaling the image. Can be overriden by `rescale_factor` in the `preprocess` + method. + """ + + model_input_names = ["pixel_values"] + + def __init__( + self, + do_resize: bool = True, + size: Dict[str, int] = None, + do_rescale: bool = True, + rescale_factor: float = 1 / 255, + **kwargs, + ) -> None: + super().__init__(**kwargs) + size = size if size is not None else {"height": 480, "width": 640} + size = get_size_dict(size, default_to_square=False) + + self.do_resize = do_resize + self.size = size + self.do_rescale = do_rescale + self.rescale_factor = rescale_factor + + def resize( + self, + image: np.ndarray, + size: Dict[str, int], + data_format: Optional[Union[str, ChannelDimension]] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + **kwargs, + ): + """ + Resize an image. + + Args: + image (`np.ndarray`): + Image to resize. + size (`Dict[str, int]`): + Dictionary of the form `{"height": int, "width": int}`, specifying the size of the output image. + data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format of the output image. If not provided, it will be inferred from the input + image. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format for the input image. If unset, the channel dimension format is inferred + from the input image. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. + """ + size = get_size_dict(size, default_to_square=False) + + return resize( + image, + size=(size["height"], size["width"]), + data_format=data_format, + input_data_format=input_data_format, + **kwargs, + ) + + def preprocess( + self, + images, + do_resize: bool = None, + size: Dict[str, int] = None, + do_rescale: bool = None, + rescale_factor: float = None, + return_tensors: Optional[Union[str, TensorType]] = None, + data_format: ChannelDimension = ChannelDimension.FIRST, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + **kwargs, + ) -> BatchFeature: + """ + Preprocess an image or batch of images. + + Args: + images (`ImageInput`): + Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If + passing in images with pixel values between 0 and 1, set `do_rescale=False`. + do_resize (`bool`, *optional*, defaults to `self.do_resize`): + Whether to resize the image. + size (`Dict[str, int]`, *optional*, defaults to `self.size`): + Size of the output image after `resize` has been applied. If `size["shortest_edge"]` >= 384, the image + is resized to `(size["shortest_edge"], size["shortest_edge"])`. Otherwise, the smaller edge of the + image will be matched to `int(size["shortest_edge"]/ crop_pct)`, after which the image is cropped to + `(size["shortest_edge"], size["shortest_edge"])`. Only has an effect if `do_resize` is set to `True`. + do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): + Whether to rescale the image values between [0 - 1]. + rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): + Rescale factor to rescale the image by if `do_rescale` is set to `True`. + return_tensors (`str` or `TensorType`, *optional*): + The type of tensors to return. Can be one of: + - Unset: Return a list of `np.ndarray`. + - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. + - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. + - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. + - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. + data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): + The channel dimension format for the output image. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - Unset: Use the channel dimension format of the input image. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format for the input image. If unset, the channel dimension format is inferred + from the input image. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. + """ + + do_resize = do_resize if do_resize is not None else self.do_resize + do_rescale = do_rescale if do_rescale is not None else self.do_rescale + rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor + + size = size if size is not None else self.size + size = get_size_dict(size, default_to_square=False) + + images = make_list_of_images(images) + + if not valid_images(images): + raise ValueError( + "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " + "torch.Tensor, tf.Tensor or jax.ndarray." + ) + + if do_resize and size is None: + raise ValueError("Size must be specified if do_resize is True.") + + if do_rescale and rescale_factor is None: + raise ValueError("Rescale factor must be specified if do_rescale is True.") + + # All transformations expect numpy arrays. + images = [to_numpy_array(image) for image in images] + + if is_scaled_image(images[0]) and do_rescale: + logger.warning_once( + "It looks like you are trying to rescale already rescaled images. If the input" + " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again." + ) + + if input_data_format is None: + # We assume that all images have the same channel dimension format. + input_data_format = infer_channel_dimension_format(images[0]) + + if do_resize: + images = [self.resize(image=image, size=size, input_data_format=input_data_format) for image in images] + + if do_rescale: + images = [ + self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) + for image in images + ] + + if input_data_format is None: + # We assume that all images have the same channel dimension format. + input_data_format = infer_channel_dimension_format(images[0]) + + # Checking if image is RGB or grayscale + for i in range(len(images)): + if not is_grayscale(images[i], input_data_format): + images[i] = convert_to_grayscale(images[i], input_data_format=input_data_format) + + images = [ + to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images + ] + + data = {"pixel_values": images} + + return BatchFeature(data=data, tensor_type=return_tensors) diff --git a/src/transformers/models/superpoint/modeling_superpoint.py b/src/transformers/models/superpoint/modeling_superpoint.py new file mode 100644 index 00000000000..dca3453486e --- /dev/null +++ b/src/transformers/models/superpoint/modeling_superpoint.py @@ -0,0 +1,507 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch SuperPoint model.""" +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import torch +from torch import nn + +from transformers import PreTrainedModel +from transformers.modeling_outputs import ( + BaseModelOutputWithNoAttention, +) +from transformers.models.superpoint.configuration_superpoint import SuperPointConfig + +from ...pytorch_utils import is_torch_greater_or_equal_than_1_13 +from ...utils import ( + ModelOutput, + add_start_docstrings, + add_start_docstrings_to_model_forward, + logging, +) + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "SuperPointConfig" + +_CHECKPOINT_FOR_DOC = "magic-leap-community/superpoint" + +SUPERPOINT_PRETRAINED_MODEL_ARCHIVE_LIST = ["magic-leap-community/superpoint"] + + +def remove_keypoints_from_borders( + keypoints: torch.Tensor, scores: torch.Tensor, border: int, height: int, width: int +) -> Tuple[torch.Tensor, torch.Tensor]: + """Removes keypoints (and their associated scores) that are too close to the border""" + mask_h = (keypoints[:, 0] >= border) & (keypoints[:, 0] < (height - border)) + mask_w = (keypoints[:, 1] >= border) & (keypoints[:, 1] < (width - border)) + mask = mask_h & mask_w + return keypoints[mask], scores[mask] + + +def top_k_keypoints(keypoints: torch.Tensor, scores: torch.Tensor, k: int) -> Tuple[torch.Tensor, torch.Tensor]: + """Keeps the k keypoints with highest score""" + if k >= len(keypoints): + return keypoints, scores + scores, indices = torch.topk(scores, k, dim=0) + return keypoints[indices], scores + + +def simple_nms(scores: torch.Tensor, nms_radius: int) -> torch.Tensor: + """Applies non-maximum suppression on scores""" + if nms_radius < 0: + raise ValueError("Expected positive values for nms_radius") + + def max_pool(x): + return nn.functional.max_pool2d(x, kernel_size=nms_radius * 2 + 1, stride=1, padding=nms_radius) + + zeros = torch.zeros_like(scores) + max_mask = scores == max_pool(scores) + for _ in range(2): + supp_mask = max_pool(max_mask.float()) > 0 + supp_scores = torch.where(supp_mask, zeros, scores) + new_max_mask = supp_scores == max_pool(supp_scores) + max_mask = max_mask | (new_max_mask & (~supp_mask)) + return torch.where(max_mask, scores, zeros) + + +@dataclass +class ImagePointDescriptionOutput(ModelOutput): + """ + Base class for outputs of image point description models. Due to the nature of keypoint detection, the number of + keypoints is not fixed and can vary from image to image, which makes batching non-trivial. In the batch of images, + the maximum number of keypoints is set as the dimension of the keypoints, scores and descriptors tensors. The mask + tensor is used to indicate which values in the keypoints, scores and descriptors tensors are keypoint information + and which are padding. + + Args: + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the decoder of the model. + keypoints (`torch.FloatTensor` of shape `(batch_size, num_keypoints, 2)`): + Relative (x, y) coordinates of predicted keypoints in a given image. + scores (`torch.FloatTensor` of shape `(batch_size, num_keypoints)`): + Scores of predicted keypoints. + descriptors (`torch.FloatTensor` of shape `(batch_size, num_keypoints, descriptor_size)`): + Descriptors of predicted keypoints. + mask (`torch.BoolTensor` of shape `(batch_size, num_keypoints)`): + Mask indicating which values in keypoints, scores and descriptors are keypoint information. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or + when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states + (also called feature maps) of the model at the output of each stage. + """ + + last_hidden_state: torch.FloatTensor = None + keypoints: Optional[torch.IntTensor] = None + scores: Optional[torch.FloatTensor] = None + descriptors: Optional[torch.FloatTensor] = None + mask: Optional[torch.BoolTensor] = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + + +class SuperPointConvBlock(nn.Module): + def __init__( + self, config: SuperPointConfig, in_channels: int, out_channels: int, add_pooling: bool = False + ) -> None: + super().__init__() + self.conv_a = nn.Conv2d( + in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + ) + self.conv_b = nn.Conv2d( + out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + ) + self.relu = nn.ReLU(inplace=True) + self.pool = nn.MaxPool2d(kernel_size=2, stride=2) if add_pooling else None + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.relu(self.conv_a(hidden_states)) + hidden_states = self.relu(self.conv_b(hidden_states)) + if self.pool is not None: + hidden_states = self.pool(hidden_states) + return hidden_states + + +class SuperPointEncoder(nn.Module): + """ + SuperPoint encoder module. It is made of 4 convolutional layers with ReLU activation and max pooling, reducing the + dimensionality of the image. + """ + + def __init__(self, config: SuperPointConfig) -> None: + super().__init__() + # SuperPoint uses 1 channel images + self.input_dim = 1 + + conv_blocks = [] + conv_blocks.append( + SuperPointConvBlock(config, self.input_dim, config.encoder_hidden_sizes[0], add_pooling=True) + ) + for i in range(1, len(config.encoder_hidden_sizes) - 1): + conv_blocks.append( + SuperPointConvBlock( + config, config.encoder_hidden_sizes[i - 1], config.encoder_hidden_sizes[i], add_pooling=True + ) + ) + conv_blocks.append( + SuperPointConvBlock( + config, config.encoder_hidden_sizes[-2], config.encoder_hidden_sizes[-1], add_pooling=False + ) + ) + self.conv_blocks = nn.ModuleList(conv_blocks) + + def forward( + self, + input, + output_hidden_states: Optional[bool] = False, + return_dict: Optional[bool] = True, + ) -> Union[Tuple, BaseModelOutputWithNoAttention]: + all_hidden_states = () if output_hidden_states else None + + for conv_block in self.conv_blocks: + input = conv_block(input) + if output_hidden_states: + all_hidden_states = all_hidden_states + (input,) + output = input + if not return_dict: + return tuple(v for v in [output, all_hidden_states] if v is not None) + + return BaseModelOutputWithNoAttention( + last_hidden_state=output, + hidden_states=all_hidden_states, + ) + + +class SuperPointInterestPointDecoder(nn.Module): + """ + The SuperPointInterestPointDecoder uses the output of the SuperPointEncoder to compute the keypoint with scores. + The scores are first computed by a convolutional layer, then a softmax is applied to get a probability distribution + over the 65 possible keypoint classes. The keypoints are then extracted from the scores by thresholding and + non-maximum suppression. Post-processing is then applied to remove keypoints too close to the image borders as well + as to keep only the k keypoints with highest score. + """ + + def __init__(self, config: SuperPointConfig) -> None: + super().__init__() + self.keypoint_threshold = config.keypoint_threshold + self.max_keypoints = config.max_keypoints + self.nms_radius = config.nms_radius + self.border_removal_distance = config.border_removal_distance + + self.relu = nn.ReLU(inplace=True) + self.pool = nn.MaxPool2d(kernel_size=2, stride=2) + self.conv_score_a = nn.Conv2d( + config.encoder_hidden_sizes[-1], + config.decoder_hidden_size, + kernel_size=3, + stride=1, + padding=1, + ) + self.conv_score_b = nn.Conv2d( + config.decoder_hidden_size, config.keypoint_decoder_dim, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, encoded: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + scores = self._get_pixel_scores(encoded) + keypoints, scores = self._extract_keypoints(scores) + + return keypoints, scores + + def _get_pixel_scores(self, encoded: torch.Tensor) -> torch.Tensor: + """Based on the encoder output, compute the scores for each pixel of the image""" + scores = self.relu(self.conv_score_a(encoded)) + scores = self.conv_score_b(scores) + scores = nn.functional.softmax(scores, 1)[:, :-1] + batch_size, _, height, width = scores.shape + scores = scores.permute(0, 2, 3, 1).reshape(batch_size, height, width, 8, 8) + scores = scores.permute(0, 1, 3, 2, 4).reshape(batch_size, height * 8, width * 8) + scores = simple_nms(scores, self.nms_radius) + return scores + + def _extract_keypoints(self, scores: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """Based on their scores, extract the pixels that represent the keypoints that will be used for descriptors computation""" + _, height, width = scores.shape + + # Threshold keypoints by score value + keypoints = torch.nonzero(scores[0] > self.keypoint_threshold) + scores = scores[0][tuple(keypoints.t())] + + # Discard keypoints near the image borders + keypoints, scores = remove_keypoints_from_borders( + keypoints, scores, self.border_removal_distance, height * 8, width * 8 + ) + + # Keep the k keypoints with highest score + if self.max_keypoints >= 0: + keypoints, scores = top_k_keypoints(keypoints, scores, self.max_keypoints) + + # Convert (y, x) to (x, y) + keypoints = torch.flip(keypoints, [1]).float() + + return keypoints, scores + + +class SuperPointDescriptorDecoder(nn.Module): + """ + The SuperPointDescriptorDecoder uses the outputs of both the SuperPointEncoder and the + SuperPointInterestPointDecoder to compute the descriptors at the keypoints locations. + + The descriptors are first computed by a convolutional layer, then normalized to have a norm of 1. The descriptors + are then interpolated at the keypoints locations. + """ + + def __init__(self, config: SuperPointConfig) -> None: + super().__init__() + + self.relu = nn.ReLU(inplace=True) + self.pool = nn.MaxPool2d(kernel_size=2, stride=2) + self.conv_descriptor_a = nn.Conv2d( + config.encoder_hidden_sizes[-1], + config.decoder_hidden_size, + kernel_size=3, + stride=1, + padding=1, + ) + self.conv_descriptor_b = nn.Conv2d( + config.decoder_hidden_size, + config.descriptor_decoder_dim, + kernel_size=1, + stride=1, + padding=0, + ) + + def forward(self, encoded: torch.Tensor, keypoints: torch.Tensor) -> torch.Tensor: + """Based on the encoder output and the keypoints, compute the descriptors for each keypoint""" + descriptors = self.conv_descriptor_b(self.relu(self.conv_descriptor_a(encoded))) + descriptors = nn.functional.normalize(descriptors, p=2, dim=1) + + descriptors = self._sample_descriptors(keypoints[None], descriptors[0][None], 8)[0] + + # [descriptor_dim, num_keypoints] -> [num_keypoints, descriptor_dim] + descriptors = torch.transpose(descriptors, 0, 1) + + return descriptors + + @staticmethod + def _sample_descriptors(keypoints, descriptors, scale: int = 8) -> torch.Tensor: + """Interpolate descriptors at keypoint locations""" + batch_size, num_channels, height, width = descriptors.shape + keypoints = keypoints - scale / 2 + 0.5 + divisor = torch.tensor([[(width * scale - scale / 2 - 0.5), (height * scale - scale / 2 - 0.5)]]) + divisor = divisor.to(keypoints) + keypoints /= divisor + keypoints = keypoints * 2 - 1 # normalize to (-1, 1) + kwargs = {"align_corners": True} if is_torch_greater_or_equal_than_1_13 else {} + # [batch_size, num_channels, num_keypoints, 2] -> [batch_size, num_channels, num_keypoints, 2] + keypoints = keypoints.view(batch_size, 1, -1, 2) + descriptors = nn.functional.grid_sample(descriptors, keypoints, mode="bilinear", **kwargs) + # [batch_size, descriptor_decoder_dim, num_channels, num_keypoints] -> [batch_size, descriptor_decoder_dim, num_keypoints] + descriptors = descriptors.reshape(batch_size, num_channels, -1) + descriptors = nn.functional.normalize(descriptors, p=2, dim=1) + return descriptors + + +class SuperPointPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = SuperPointConfig + base_model_prefix = "superpoint" + main_input_name = "pixel_values" + supports_gradient_checkpointing = False + + def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None: + """Initialize the weights""" + if isinstance(module, (nn.Linear, nn.Conv2d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def extract_one_channel_pixel_values(self, pixel_values: torch.FloatTensor) -> torch.FloatTensor: + """ + Assuming pixel_values has shape (batch_size, 3, height, width), and that all channels values are the same, + extract the first channel value to get a tensor of shape (batch_size, 1, height, width) for SuperPoint. This is + a workaround for the issue discussed in : + https://github.com/huggingface/transformers/pull/25786#issuecomment-1730176446 + + Args: + pixel_values: torch.FloatTensor of shape (batch_size, 3, height, width) + + Returns: + pixel_values: torch.FloatTensor of shape (batch_size, 1, height, width) + + """ + return pixel_values[:, 0, :, :][:, None, :, :] + + +SUPERPOINT_START_DOCSTRING = r""" + This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it + as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and + behavior. + + Parameters: + config ([`SuperPointConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. + """ + +SUPERPOINT_INPUTS_DOCSTRING = r""" +Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using [`SuperPointImageProcessor`]. See + [`SuperPointImageProcessor.__call__`] for details. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more + detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + + +@add_start_docstrings( + "SuperPoint model outputting keypoints and descriptors.", + SUPERPOINT_START_DOCSTRING, +) +class SuperPointModel(SuperPointPreTrainedModel): + """ + SuperPoint model. It consists of a SuperPointEncoder, a SuperPointInterestPointDecoder and a + SuperPointDescriptorDecoder. SuperPoint was proposed in `SuperPoint: Self-Supervised Interest Point Detection and + Description `__ by Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. It + is a fully convolutional neural network that extracts keypoints and descriptors from an image. It is trained in a + self-supervised manner, using a combination of a photometric loss and a loss based on the homographic adaptation of + keypoints. It is made of a convolutional encoder and two decoders: one for keypoints and one for descriptors. + """ + + def __init__(self, config: SuperPointConfig) -> None: + super().__init__(config) + + self.config = config + + self.encoder = SuperPointEncoder(config) + self.keypoint_decoder = SuperPointInterestPointDecoder(config) + self.descriptor_decoder = SuperPointDescriptorDecoder(config) + + self.post_init() + + @add_start_docstrings_to_model_forward(SUPERPOINT_INPUTS_DOCSTRING) + def forward( + self, + pixel_values: torch.FloatTensor = None, + labels: Optional[torch.LongTensor] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, ImagePointDescriptionOutput]: + """ + Examples: + + ```python + >>> from transformers import AutoImageProcessor, AutoModel + >>> import torch + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> processor = AutoImageProcessor.from_pretrained("magic-leap-community/superpoint") + >>> model = AutoModel.from_pretrained("magic-leap-community/superpoint") + + >>> inputs = processor(image, return_tensors="pt") + >>> outputs = model(**inputs) + ```""" + + if labels is not None: + raise ValueError( + f"SuperPoint is not trainable, no labels should be provided.Therefore, labels should be None but were {type(labels)}" + ) + + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + pixel_values = self.extract_one_channel_pixel_values(pixel_values) + + batch_size = pixel_values.shape[0] + + encoder_outputs = self.encoder( + pixel_values, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + last_hidden_state = encoder_outputs[0] + + list_keypoints_scores = [ + self.keypoint_decoder(last_hidden_state[None, ...]) for last_hidden_state in last_hidden_state + ] + + list_keypoints = [keypoints_scores[0] for keypoints_scores in list_keypoints_scores] + list_scores = [keypoints_scores[1] for keypoints_scores in list_keypoints_scores] + + list_descriptors = [ + self.descriptor_decoder(last_hidden_state[None, ...], keypoints[None, ...]) + for last_hidden_state, keypoints in zip(last_hidden_state, list_keypoints) + ] + + maximum_num_keypoints = max(keypoints.shape[0] for keypoints in list_keypoints) + + keypoints = torch.zeros((batch_size, maximum_num_keypoints, 2), device=pixel_values.device) + scores = torch.zeros((batch_size, maximum_num_keypoints), device=pixel_values.device) + descriptors = torch.zeros( + (batch_size, maximum_num_keypoints, self.config.descriptor_decoder_dim), + device=pixel_values.device, + ) + mask = torch.zeros((batch_size, maximum_num_keypoints), device=pixel_values.device, dtype=torch.int) + + for i, (_keypoints, _scores, _descriptors) in enumerate(zip(list_keypoints, list_scores, list_descriptors)): + keypoints[i, : _keypoints.shape[0]] = _keypoints + scores[i, : _scores.shape[0]] = _scores + descriptors[i, : _descriptors.shape[0]] = _descriptors + mask[i, : _scores.shape[0]] = 1 + + hidden_states = encoder_outputs[1] if output_hidden_states else None + if not return_dict: + return tuple( + v for v in [last_hidden_state, keypoints, scores, descriptors, mask, hidden_states] if v is not None + ) + + return ImagePointDescriptionOutput( + last_hidden_state=last_hidden_state, + keypoints=keypoints, + scores=scores, + descriptors=descriptors, + mask=mask, + hidden_states=hidden_states, + ) diff --git a/src/transformers/utils/dummy_pt_objects.py b/src/transformers/utils/dummy_pt_objects.py index 50c3c7ea8b4..7299ac30895 100644 --- a/src/transformers/utils/dummy_pt_objects.py +++ b/src/transformers/utils/dummy_pt_objects.py @@ -8026,6 +8026,23 @@ class Starcoder2PreTrainedModel(metaclass=DummyObject): requires_backends(self, ["torch"]) +SUPERPOINT_PRETRAINED_MODEL_ARCHIVE_LIST = None + + +class SuperPointModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + +class SuperPointPreTrainedModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None diff --git a/src/transformers/utils/dummy_vision_objects.py b/src/transformers/utils/dummy_vision_objects.py index 25a35558fe9..39012eea45d 100644 --- a/src/transformers/utils/dummy_vision_objects.py +++ b/src/transformers/utils/dummy_vision_objects.py @@ -485,6 +485,13 @@ class SiglipImageProcessor(metaclass=DummyObject): requires_backends(self, ["vision"]) +class SuperPointImageProcessor(metaclass=DummyObject): + _backends = ["vision"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["vision"]) + + class Swin2SRImageProcessor(metaclass=DummyObject): _backends = ["vision"] diff --git a/tests/fixtures/tests_samples/COCO/000000004016.png b/tests/fixtures/tests_samples/COCO/000000004016.png new file mode 100644 index 0000000000000000000000000000000000000000..f119422aa6705be46794ca91dfaefd1387bf5050 GIT binary patch literal 636411 zcmV)lK%c*fP)P6!E*5Q->+avIHOa?UwwMmdX|!GOWoUfbXt*537c*VzPvM3RsY z63PMP3>fpBo&haud*A!s=Xvk$etY*k=P8}8sqX1+b=9x_-PJXEdjCz+*Vq4L*1xbB z7#J+2OZJsHhSFRt~QTlg2W7xx?jLqo$~9LTTD)YR0{(sJp*m(u@j zw%m0kg^w$%SiIgr-GgxvmM?@j<&?GjBz|5!fikJc%PfC^)v*l%cWihw7LZGY_ zsOv-}HNvt=fl9+tm+@8A5?Q%GRV|g23I)Y%MWslfrU^AveleM+qKHaZ0u39SSIiWY za=2**etn|(Mt^KgFQr>W_M*||q^^N(j2ewvwoJpbs)?Be5@ z>4mwO`RTE#iGktauKRuMcYE8~@3*vex87;L(R}|-OZ$zc8yA1Lb@|H8AFf>g`cm6B zO*g-9y3=yEuYY*t!NmC3^yJjs*wpOg#N7DU%;?zk$k;Tb4@Rd(hQ}Waj}H%y4h=kj 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zSzDiX_$Kr^Tg;XV{%f$EuB`c(I?uUV{v;#f@_xxtG#{*%YvRj$VaO_=6%4P80c)es z)H7h-;qo)>m`|EzgG%lcTjpthW*lAa5E-B9X-C~7 z0KoQ4^1d)rebVEw8L=pzC1YnEtWo$G@ zhoS%;7LRL7-1mUZwomY{i5V(7p7-h2p0x0nRPfj(i1iCN?P6F3&yEJ$LH}(se#f4P zgSV^cuKGq;Pl(r&!?cDCzfv~Kw6Vx;n-Al|AA;uhAi>1o)+3={(tea4J`NiMV8HOS zPO}1}obOuhm7rIo7APCGUQ8RNOld_*v+gTwB-YmOK~JX*;;v}1pc>1t{s^Bktvv+p z0_kx*Ai-hdVN`z*RPRrUcMXH@gwvmMXL9veVU20fX@4@2QeY`8xWqPDbY|uO0 z1I@jNok4ul_PufS_M~IDq(OQk00000NkvXXu0mjf12my4 literal 0 HcmV?d00001 diff --git a/tests/models/superpoint/__init__.py b/tests/models/superpoint/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/models/superpoint/test_image_processing_superpoint.py b/tests/models/superpoint/test_image_processing_superpoint.py new file mode 100644 index 00000000000..19406bc91ad --- /dev/null +++ b/tests/models/superpoint/test_image_processing_superpoint.py @@ -0,0 +1,111 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import unittest + +import numpy as np + +from transformers.testing_utils import require_torch, require_vision +from transformers.utils import is_vision_available + +from ...test_image_processing_common import ( + ImageProcessingTestMixin, + prepare_image_inputs, +) + + +if is_vision_available(): + from transformers import SuperPointImageProcessor + + +class SuperPointImageProcessingTester(unittest.TestCase): + def __init__( + self, + parent, + batch_size=7, + num_channels=3, + image_size=18, + min_resolution=30, + max_resolution=400, + do_resize=True, + size=None, + ): + size = size if size is not None else {"height": 480, "width": 640} + self.parent = parent + self.batch_size = batch_size + self.num_channels = num_channels + self.image_size = image_size + self.min_resolution = min_resolution + self.max_resolution = max_resolution + self.do_resize = do_resize + self.size = size + + def prepare_image_processor_dict(self): + return { + "do_resize": self.do_resize, + "size": self.size, + } + + def expected_output_image_shape(self, images): + return self.num_channels, self.size["height"], self.size["width"] + + def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False): + return prepare_image_inputs( + batch_size=self.batch_size, + num_channels=self.num_channels, + min_resolution=self.min_resolution, + max_resolution=self.max_resolution, + equal_resolution=equal_resolution, + numpify=numpify, + torchify=torchify, + ) + + +@require_torch +@require_vision +class SuperPointImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): + image_processing_class = SuperPointImageProcessor if is_vision_available() else None + + def setUp(self) -> None: + self.image_processor_tester = SuperPointImageProcessingTester(self) + + @property + def image_processor_dict(self): + return self.image_processor_tester.prepare_image_processor_dict() + + def test_image_processing(self): + image_processing = self.image_processing_class(**self.image_processor_dict) + self.assertTrue(hasattr(image_processing, "do_resize")) + self.assertTrue(hasattr(image_processing, "size")) + self.assertTrue(hasattr(image_processing, "do_rescale")) + self.assertTrue(hasattr(image_processing, "rescale_factor")) + + def test_image_processor_from_dict_with_kwargs(self): + image_processor = self.image_processing_class.from_dict(self.image_processor_dict) + self.assertEqual(image_processor.size, {"height": 480, "width": 640}) + + image_processor = self.image_processing_class.from_dict( + self.image_processor_dict, size={"height": 42, "width": 42} + ) + self.assertEqual(image_processor.size, {"height": 42, "width": 42}) + + @unittest.skip(reason="SuperPointImageProcessor is always supposed to return a grayscaled image") + def test_call_numpy_4_channels(self): + pass + + def test_input_image_properly_converted_to_grayscale(self): + image_processor = self.image_processing_class.from_dict(self.image_processor_dict) + image_inputs = self.image_processor_tester.prepare_image_inputs() + pre_processed_images = image_processor.preprocess(image_inputs) + for image in pre_processed_images["pixel_values"]: + self.assertTrue(np.all(image[0, ...] == image[1, ...]) and np.all(image[1, ...] == image[2, ...])) diff --git a/tests/models/superpoint/test_modeling_superpoint.py b/tests/models/superpoint/test_modeling_superpoint.py new file mode 100644 index 00000000000..f505096ea3e --- /dev/null +++ b/tests/models/superpoint/test_modeling_superpoint.py @@ -0,0 +1,307 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import inspect +import unittest +from typing import List + +from transformers.models.superpoint.configuration_superpoint import SuperPointConfig +from transformers.testing_utils import require_torch, require_vision, slow, torch_device +from transformers.utils import cached_property, is_torch_available, is_vision_available + +from ...test_configuration_common import ConfigTester +from ...test_modeling_common import ModelTesterMixin, floats_tensor + + +if is_torch_available(): + import torch + + from transformers import ( + SUPERPOINT_PRETRAINED_MODEL_ARCHIVE_LIST, + SuperPointModel, + ) + +if is_vision_available(): + from PIL import Image + + from transformers import AutoImageProcessor + + +class SuperPointModelTester: + def __init__( + self, + parent, + batch_size=3, + image_width=80, + image_height=60, + encoder_hidden_sizes: List[int] = [32, 32, 64, 64], + decoder_hidden_size: int = 128, + keypoint_decoder_dim: int = 65, + descriptor_decoder_dim: int = 128, + keypoint_threshold: float = 0.005, + max_keypoints: int = -1, + nms_radius: int = 4, + border_removal_distance: int = 4, + ): + self.parent = parent + self.batch_size = batch_size + self.image_width = image_width + self.image_height = image_height + + self.encoder_hidden_sizes = encoder_hidden_sizes + self.decoder_hidden_size = decoder_hidden_size + self.keypoint_decoder_dim = keypoint_decoder_dim + self.descriptor_decoder_dim = descriptor_decoder_dim + self.keypoint_threshold = keypoint_threshold + self.max_keypoints = max_keypoints + self.nms_radius = nms_radius + self.border_removal_distance = border_removal_distance + + def prepare_config_and_inputs(self): + # SuperPoint expects a grayscale image as input + pixel_values = floats_tensor([self.batch_size, 3, self.image_height, self.image_width]) + config = self.get_config() + return config, pixel_values + + def get_config(self): + return SuperPointConfig( + encoder_hidden_sizes=self.encoder_hidden_sizes, + decoder_hidden_size=self.decoder_hidden_size, + keypoint_decoder_dim=self.keypoint_decoder_dim, + descriptor_decoder_dim=self.descriptor_decoder_dim, + keypoint_threshold=self.keypoint_threshold, + max_keypoints=self.max_keypoints, + nms_radius=self.nms_radius, + border_removal_distance=self.border_removal_distance, + ) + + def create_and_check_model(self, config, pixel_values): + model = SuperPointModel(config=config) + model.to(torch_device) + model.eval() + result = model(pixel_values) + self.parent.assertEqual( + result.last_hidden_state.shape, + ( + self.batch_size, + self.encoder_hidden_sizes[-1], + self.image_height // 8, + self.image_width // 8, + ), + ) + + def prepare_config_and_inputs_for_common(self): + config_and_inputs = self.prepare_config_and_inputs() + config, pixel_values = config_and_inputs + inputs_dict = {"pixel_values": pixel_values} + return config, inputs_dict + + +@require_torch +class SuperPointModelTest(ModelTesterMixin, unittest.TestCase): + all_model_classes = (SuperPointModel,) if is_torch_available() else () + all_generative_model_classes = () if is_torch_available() else () + + fx_compatible = False + test_pruning = False + test_resize_embeddings = False + test_head_masking = False + has_attentions = False + + def setUp(self): + self.model_tester = SuperPointModelTester(self) + self.config_tester = ConfigTester(self, config_class=SuperPointConfig, has_text_modality=False, hidden_size=37) + + def test_config(self): + self.create_and_test_config_common_properties() + self.config_tester.create_and_test_config_to_json_string() + self.config_tester.create_and_test_config_to_json_file() + self.config_tester.create_and_test_config_from_and_save_pretrained() + self.config_tester.create_and_test_config_with_num_labels() + self.config_tester.check_config_can_be_init_without_params() + self.config_tester.check_config_arguments_init() + + def create_and_test_config_common_properties(self): + return + + @unittest.skip(reason="SuperPointModel does not use inputs_embeds") + def test_inputs_embeds(self): + pass + + @unittest.skip(reason="SuperPointModel does not support input and output embeddings") + def test_model_common_attributes(self): + pass + + @unittest.skip(reason="SuperPointModel does not use feedforward chunking") + def test_feed_forward_chunking(self): + pass + + @unittest.skip(reason="SuperPointModel is not trainable") + def test_training(self): + pass + + @unittest.skip(reason="SuperPointModel is not trainable") + def test_training_gradient_checkpointing(self): + pass + + @unittest.skip(reason="SuperPointModel is not trainable") + def test_training_gradient_checkpointing_use_reentrant(self): + pass + + @unittest.skip(reason="SuperPointModel is not trainable") + def test_training_gradient_checkpointing_use_reentrant_false(self): + pass + + @unittest.skip(reason="SuperPoint does not output any loss term in the forward pass") + def test_retain_grad_hidden_states_attentions(self): + pass + + def test_model(self): + config_and_inputs = self.model_tester.prepare_config_and_inputs() + self.model_tester.create_and_check_model(*config_and_inputs) + + def test_forward_signature(self): + config, _ = self.model_tester.prepare_config_and_inputs() + + for model_class in self.all_model_classes: + model = model_class(config) + signature = inspect.signature(model.forward) + # signature.parameters is an OrderedDict => so arg_names order is deterministic + arg_names = [*signature.parameters.keys()] + + expected_arg_names = ["pixel_values"] + self.assertListEqual(arg_names[:1], expected_arg_names) + + def test_hidden_states_output(self): + def check_hidden_states_output(inputs_dict, config, model_class): + model = model_class(config) + model.to(torch_device) + model.eval() + + with torch.no_grad(): + outputs = model(**self._prepare_for_class(inputs_dict, model_class)) + + hidden_states = outputs.hidden_states + + # SuperPoint's feature maps are of shape (batch_size, num_channels, width, height) + for i, conv_layer_size in enumerate(self.model_tester.encoder_hidden_sizes[:-1]): + self.assertListEqual( + list(hidden_states[i].shape[-3:]), + [ + conv_layer_size, + self.model_tester.image_height // (2 ** (i + 1)), + self.model_tester.image_width // (2 ** (i + 1)), + ], + ) + + config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() + + for model_class in self.all_model_classes: + inputs_dict["output_hidden_states"] = True + check_hidden_states_output(inputs_dict, config, model_class) + + # check that output_hidden_states also work using config + del inputs_dict["output_hidden_states"] + config.output_hidden_states = True + + check_hidden_states_output(inputs_dict, config, model_class) + + @slow + def test_model_from_pretrained(self): + for model_name in SUPERPOINT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: + model = SuperPointModel.from_pretrained(model_name) + self.assertIsNotNone(model) + + def test_forward_labels_should_be_none(self): + config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() + for model_class in self.all_model_classes: + model = model_class(config) + model.to(torch_device) + model.eval() + + with torch.no_grad(): + model_inputs = self._prepare_for_class(inputs_dict, model_class) + # Provide an arbitrary sized Tensor as labels to model inputs + model_inputs["labels"] = torch.rand((128, 128)) + + with self.assertRaises(ValueError) as cm: + model(**model_inputs) + self.assertEqual(ValueError, cm.exception.__class__) + + +def prepare_imgs(): + image1 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") + image2 = Image.open("./tests/fixtures/tests_samples/COCO/000000004016.png") + return [image1, image2] + + +@require_torch +@require_vision +class SuperPointModelIntegrationTest(unittest.TestCase): + @cached_property + def default_image_processor(self): + return AutoImageProcessor.from_pretrained("magic-leap-community/superpoint") if is_vision_available() else None + + @slow + def test_inference(self): + model = SuperPointModel.from_pretrained("magic-leap-community/superpoint").to(torch_device) + preprocessor = self.default_image_processor + images = prepare_imgs() + inputs = preprocessor(images=images, return_tensors="pt").to(torch_device) + with torch.no_grad(): + outputs = model(**inputs) + expected_number_keypoints_image0 = 567 + expected_number_keypoints_image1 = 830 + expected_max_number_keypoints = max(expected_number_keypoints_image0, expected_number_keypoints_image1) + expected_keypoints_shape = torch.Size((len(images), expected_max_number_keypoints, 2)) + expected_scores_shape = torch.Size( + ( + len(images), + expected_max_number_keypoints, + ) + ) + expected_descriptors_shape = torch.Size((len(images), expected_max_number_keypoints, 256)) + # Check output shapes + self.assertEqual(outputs.keypoints.shape, expected_keypoints_shape) + self.assertEqual(outputs.scores.shape, expected_scores_shape) + self.assertEqual(outputs.descriptors.shape, expected_descriptors_shape) + expected_keypoints_image0_values = torch.tensor([[480.0, 9.0], [494.0, 9.0], [489.0, 16.0]]).to(torch_device) + expected_scores_image0_values = torch.tensor( + [0.0064, 0.0137, 0.0589, 0.0723, 0.5166, 0.0174, 0.1515, 0.2054, 0.0334] + ).to(torch_device) + expected_descriptors_image0_value = torch.tensor(-0.1096).to(torch_device) + predicted_keypoints_image0_values = outputs.keypoints[0, :3] + predicted_scores_image0_values = outputs.scores[0, :9] + predicted_descriptors_image0_value = outputs.descriptors[0, 0, 0] + # Check output values + self.assertTrue( + torch.allclose( + predicted_keypoints_image0_values, + expected_keypoints_image0_values, + atol=1e-4, + ) + ) + self.assertTrue(torch.allclose(predicted_scores_image0_values, expected_scores_image0_values, atol=1e-4)) + self.assertTrue( + torch.allclose( + predicted_descriptors_image0_value, + expected_descriptors_image0_value, + atol=1e-4, + ) + ) + # Check mask values + self.assertTrue(outputs.mask[0, expected_number_keypoints_image0 - 1].item() == 1) + self.assertTrue(outputs.mask[0, expected_number_keypoints_image0].item() == 0) + self.assertTrue(torch.all(outputs.mask[0, : expected_number_keypoints_image0 - 1])) + self.assertTrue(torch.all(torch.logical_not(outputs.mask[0, expected_number_keypoints_image0:]))) + self.assertTrue(torch.all(outputs.mask[1]))