Add SigLIP (#26522)
* Add first draft * Use appropriate gelu function * More improvements * More improvements * More improvements * Convert checkpoint * More improvements * Improve docs, remove print statements * More improvements * Add link * remove unused masking function * begin tokenizer * do_lower_case * debug * set split_special_tokens=True * Remove script * Fix style * Fix rebase * Use same design as CLIP * Add fast tokenizer * Add SiglipTokenizer to init, remove extra_ids * Improve conversion script * Use smaller inputs in conversion script * Update conversion script * More improvements * Add processor to conversion script * Add tests * Remove print statements * Add tokenizer tests * Fix more tests * More improvements related to weight initialization * More improvements * Make more tests pass * More improvements * More improvements * Add copied from * Add canonicalize_text * Enable fast tokenizer tests * More improvements * Fix most slow tokenizer tests * Address comments * Fix style * Remove script * Address some comments * Add copied from to tests * Add more copied from * Add more copied from * Add more copied from * Remove is_flax_available * More updates * Address comment * Remove SiglipTokenizerFast for now * Add caching * Remove umt5 test * Add canonicalize_text inside _tokenize, thanks Arthur * Fix image processor tests * Skip tests which are not applicable * Skip test_initialization * More improvements * Compare pixel values * Fix doc tests, add integration test * Add do_normalize * Remove causal mask and leverage ignore copy * Fix attention_mask * Fix remaining tests * Fix dummies * Rename temperature and bias * Address comments * Add copied from to tokenizer tests * Add SiglipVisionModel to auto mapping * Add copied from to image processor tests * Improve doc * Remove SiglipVisionModel from index * Address comments * Improve docs * Simplify config * Add first draft * Make it like mistral * More improvements * Fix attention_mask * Fix output_attentions * Add note in docs * Convert multilingual model * Convert large checkpoint * Convert more checkpoints * Add pipeline support, correct image_mean and image_std * Use padding=max_length by default * Make processor like llava * Add code snippet * Convert more checkpoints * Set keep_punctuation_string=None as in OpenCLIP * Set normalized=False for special tokens * Fix doc test * Update integration test * Add figure * Update organization * Happy new year * Use AutoModel everywhere --------- Co-authored-by: patil-suraj <surajp815@gmail.com>
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NielsRogge
GitHub
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@ -475,6 +475,7 @@ Current number of checkpoints: ** (from Meta AI) released with the paper [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
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1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
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1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
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1. **[SigLIP](https://huggingface.co/docs/transformers/main/model_doc/siglip)** (from Google AI) released with the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer.
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1. **[SpeechT5](https://huggingface.co/docs/transformers/model_doc/speecht5)** (from Microsoft Research) released with the paper [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://arxiv.org/abs/2110.07205) by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.
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1. **[SpeechToTextTransformer](https://huggingface.co/docs/transformers/model_doc/speech_to_text)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
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1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (from Facebook), released together with the paper [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
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@ -450,6 +450,7 @@ Número actual de puntos de control: ** (from Meta AI) released with the paper [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
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1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
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1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
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1. **[SigLIP](https://huggingface.co/docs/transformers/main/model_doc/siglip)** (from Google AI) released with the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer.
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1. **[SpeechT5](https://huggingface.co/docs/transformers/model_doc/speecht5)** (from Microsoft Research) released with the paper [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://arxiv.org/abs/2110.07205) by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.
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1. **[SpeechToTextTransformer](https://huggingface.co/docs/transformers/model_doc/speech_to_text)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
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1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (from Facebook), released together with the paper [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
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@ -424,6 +424,7 @@ conda install conda-forge::transformers
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1. **[Segment Anything](https://huggingface.co/docs/transformers/model_doc/sam)** (Meta AI से) Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick. द्वाराअनुसंधान पत्र [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) के साथ जारी किया गया
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1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (ASAPP से) साथ देने वाला पेपर [भाषण पहचान के लिए अनसुपरवाइज्ड प्री-ट्रेनिंग में परफॉर्मेंस-एफिशिएंसी ट्रेड-ऑफ्स](https ://arxiv.org/abs/2109.06870) फेलिक्स वू, क्वांगयुन किम, जिंग पैन, क्यू हान, किलियन क्यू. वेनबर्गर, योव आर्टज़ी द्वारा।
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1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (ASAPP से) साथ में पेपर [भाषण पहचान के लिए अनसुपरवाइज्ड प्री-ट्रेनिंग में परफॉर्मेंस-एफिशिएंसी ट्रेड-ऑफ्स] (https://arxiv.org/abs/2109.06870) फेलिक्स वू, क्वांगयुन किम, जिंग पैन, क्यू हान, किलियन क्यू. वेनबर्गर, योआव आर्टज़ी द्वारा पोस्ट किया गया।
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1. **[SigLIP](https://huggingface.co/docs/transformers/main/model_doc/siglip)** (Google AI से) Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer. द्वाराअनुसंधान पत्र [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) के साथ जारी किया गया
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1. **[SpeechT5](https://huggingface.co/docs/transformers/model_doc/speecht5)** (from Microsoft Research) released with the paper [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://arxiv.org/abs/2110.07205) by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.
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1. **[SpeechToTextTransformer](https://huggingface.co/docs/transformers/model_doc/speech_to_text)** (फेसबुक से), साथ में पेपर [फेयरसेक S2T: फास्ट स्पीच-टू-टेक्स्ट मॉडलिंग विद फेयरसेक](https: //arxiv.org/abs/2010.05171) चांगहान वांग, यूं तांग, जुताई मा, ऐनी वू, दिमित्रो ओखोनको, जुआन पिनो द्वारा पोस्ट किया गया。
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1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (फेसबुक से) साथ में पेपर [लार्ज-स्केल सेल्फ- एंड सेमी-सुपरवाइज्ड लर्निंग फॉर स्पीच ट्रांसलेशन](https://arxiv.org/abs/2104.06678) चांगहान वांग, ऐनी वू, जुआन पिनो, एलेक्सी बेवस्की, माइकल औली, एलेक्सिस द्वारा Conneau द्वारा पोस्ट किया गया।
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@ -484,6 +484,7 @@ Flax、PyTorch、TensorFlowをcondaでインストールする方法は、それ
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1. **[Segment Anything](https://huggingface.co/docs/transformers/model_doc/sam)** (Meta AI から) Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick. から公開された研究論文 [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf)
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1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (ASAPP から) Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi から公開された研究論文: [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870)
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1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (ASAPP から) Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi から公開された研究論文: [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870)
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1. **[SigLIP](https://huggingface.co/docs/transformers/main/model_doc/siglip)** (Google AI から) Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer. から公開された研究論文 [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343)
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1. **[SpeechT5](https://huggingface.co/docs/transformers/model_doc/speecht5)** (Microsoft Research から) Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei. から公開された研究論文 [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://arxiv.org/abs/2110.07205)
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1. **[SpeechToTextTransformer](https://huggingface.co/docs/transformers/model_doc/speech_to_text)** (Facebook から), Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino から公開された研究論文: [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171)
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1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (Facebook から), Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau から公開された研究論文: [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678)
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@ -399,6 +399,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
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1. **[Segment Anything](https://huggingface.co/docs/transformers/model_doc/sam)** (Meta AI 에서 제공)은 Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.의 [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf)논문과 함께 발표했습니다.
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1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (ASAPP 에서) Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi 의 [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) 논문과 함께 발표했습니다.
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1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (ASAPP 에서) Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi 의 [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) 논문과 함께 발표했습니다.
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1. **[SigLIP](https://huggingface.co/docs/transformers/main/model_doc/siglip)** (Google AI 에서 제공)은 Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer.의 [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343)논문과 함께 발표했습니다.
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1. **[SpeechT5](https://huggingface.co/docs/transformers/model_doc/speecht5)** (Microsoft Research 에서 제공)은 Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.의 [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://arxiv.org/abs/2110.07205)논문과 함께 발표했습니다.
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1. **[SpeechToTextTransformer](https://huggingface.co/docs/transformers/model_doc/speech_to_text)** (Facebook 에서) Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino 의 [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) 논문과 함께 발표했습니다.
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1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (Facebook 에서) Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau 의 [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) 논문과 함께 발표했습니다.
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@ -423,6 +423,7 @@ conda install conda-forge::transformers
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1. **[Segment Anything](https://huggingface.co/docs/transformers/model_doc/sam)** (来自 Meta AI) 伴随论文 [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) 由 Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick 发布。
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1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (来自 ASAPP) 伴随论文 [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) 由 Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi 发布。
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1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (来自 ASAPP) 伴随论文 [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) 由 Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi 发布。
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1. **[SigLIP](https://huggingface.co/docs/transformers/main/model_doc/siglip)** (来自 Google AI) 伴随论文 [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) 由 Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer 发布。
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1. **[SpeechT5](https://huggingface.co/docs/transformers/model_doc/speecht5)** (来自 Microsoft Research) 伴随论文 [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://arxiv.org/abs/2110.07205) 由 Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei 发布。
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1. **[SpeechToTextTransformer](https://huggingface.co/docs/transformers/model_doc/speech_to_text)** (来自 Facebook), 伴随论文 [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) 由 Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino 发布。
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1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (来自 Facebook) 伴随论文 [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) 由 Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau 发布。
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@ -435,6 +435,7 @@ conda install conda-forge::transformers
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1. **[Segment Anything](https://huggingface.co/docs/transformers/model_doc/sam)** (from Meta AI) released with the paper [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
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1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
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1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
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1. **[SigLIP](https://huggingface.co/docs/transformers/main/model_doc/siglip)** (from Google AI) released with the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer.
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1. **[SpeechT5](https://huggingface.co/docs/transformers/model_doc/speecht5)** (from Microsoft Research) released with the paper [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://arxiv.org/abs/2110.07205) by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.
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1. **[SpeechToTextTransformer](https://huggingface.co/docs/transformers/model_doc/speech_to_text)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
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1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (from Facebook) released with the paper [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
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title: Pix2Struct
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- local: model_doc/sam
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title: Segment Anything
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- local: model_doc/siglip
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title: SigLIP
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- local: model_doc/speech-encoder-decoder
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title: Speech Encoder Decoder Models
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- local: model_doc/tapas
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@ -250,6 +250,7 @@ Flax), PyTorch, and/or TensorFlow.
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| [SegFormer](model_doc/segformer) | ✅ | ✅ | ❌ |
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| [SEW](model_doc/sew) | ✅ | ❌ | ❌ |
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| [SEW-D](model_doc/sew-d) | ✅ | ❌ | ❌ |
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| [SigLIP](model_doc/siglip) | ✅ | ❌ | ❌ |
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| [Speech Encoder decoder](model_doc/speech-encoder-decoder) | ✅ | ❌ | ✅ |
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| [Speech2Text](model_doc/speech_to_text) | ✅ | ✅ | ❌ |
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| [SpeechT5](model_doc/speecht5) | ✅ | ❌ | ❌ |
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@ -0,0 +1,141 @@
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<!--Copyright 2023 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
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.
|
||||
|
||||
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
|
||||
rendered properly in your Markdown viewer.
|
||||
|
||||
-->
|
||||
|
||||
# SigLIP
|
||||
|
||||
## Overview
|
||||
|
||||
The SigLIP model was proposed in [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer. SigLIP proposes to replace the loss function used in [CLIP](clip) by a simple pairwise sigmoid loss. This results in better performance in terms of zero-shot classification accuracy on ImageNet.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*We propose a simple pairwise Sigmoid loss for Language-Image Pre-training (SigLIP). Unlike standard contrastive learning with softmax normalization, the sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities for normalization. The sigmoid loss simultaneously allows further scaling up the batch size, while also performing better at smaller batch sizes. Combined with Locked-image Tuning, with only four TPUv4 chips, we train a SigLiT model that achieves 84.5% ImageNet zero-shot accuracy in two days. The disentanglement of the batch size from the loss further allows us to study the impact of examples vs pairs and negative to positive ratio. Finally, we push the batch size to the extreme, up to one million, and find that the benefits of growing batch size quickly diminish, with a more reasonable batch size of 32k being sufficient.*
|
||||
|
||||
## Usage tips
|
||||
|
||||
- Usage of SigLIP is similar to [CLIP](clip). The main difference is the training loss, which does not require a global view of all the pairwise similarities of images and texts within a batch. One needs to apply the sigmoid activation function to the logits, rather than the softmax.
|
||||
- Training is not yet supported. If you want to fine-tune SigLIP or train from scratch, refer to the loss function from [OpenCLIP](https://github.com/mlfoundations/open_clip/blob/73ad04ae7fb93ede1c02dc9040a828634cb1edf1/src/open_clip/loss.py#L307), which leverages various `torch.distributed` utilities.
|
||||
- When using the standalone [`SiglipTokenizer`], make sure to pass `padding="max_length"` as that's how the model was trained. The multimodal [`SiglipProcessor`] takes care of this behind the scenes.
|
||||
|
||||
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/siglip_table.jpeg"
|
||||
alt="drawing" width="600"/>
|
||||
|
||||
<small> SigLIP evaluation results compared to CLIP. Taken from the <a href="https://arxiv.org/abs/2303.15343">original paper</a>.</small>
|
||||
|
||||
This model was contributed by [nielsr](https://huggingface.co/nielsr).
|
||||
The original code can be found [here](https://github.com/google-research/big_vision/tree/main).
|
||||
|
||||
## Usage example
|
||||
|
||||
There are 2 main ways to use SigLIP: either using the pipeline API, which abstracts away all the complexity for you, or by using the `SiglipModel` class yourself.
|
||||
|
||||
### Pipeline API
|
||||
|
||||
The pipeline allows to use the model in a few lines of code:
|
||||
|
||||
```python
|
||||
>>> from transformers import pipeline
|
||||
>>> from PIL import Image
|
||||
>>> import requests
|
||||
|
||||
>>> # load pipe
|
||||
>>> image_classifier = pipeline(task="zero-shot-image-classification", model="google/siglip-base-patch16-224")
|
||||
|
||||
>>> # load image
|
||||
>>> url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
|
||||
>>> image = Image.open(requests.get(url, stream=True).raw)
|
||||
|
||||
>>> # inference
|
||||
>>> outputs = image_classifier(image, candidate_labels=["2 cats", "a plane", "a remote"])
|
||||
>>> outputs = [{"score": round(output["score"], 4), "label": output["label"] } for output in outputs]
|
||||
>>> print(outputs)
|
||||
[{'score': 0.1979, 'label': '2 cats'}, {'score': 0.0, 'label': 'a remote'}, {'score': 0.0, 'label': 'a plane'}]
|
||||
```
|
||||
|
||||
### Using the model yourself
|
||||
|
||||
If you want to do the pre- and postprocessing yourself, here's how to do that:
|
||||
|
||||
```python
|
||||
>>> from PIL import Image
|
||||
>>> import requests
|
||||
>>> from transformers import AutoProcessor, AutoModel
|
||||
>>> import torch
|
||||
|
||||
>>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224")
|
||||
>>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224")
|
||||
|
||||
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
||||
>>> image = Image.open(requests.get(url, stream=True).raw)
|
||||
|
||||
>>> texts = ["a photo of 2 cats", "a photo of 2 dogs"]
|
||||
>>> inputs = processor(text=texts, images=image, return_tensors="pt")
|
||||
|
||||
>>> with torch.no_grad():
|
||||
... outputs = model(**inputs)
|
||||
|
||||
>>> logits_per_image = outputs.logits_per_image
|
||||
>>> probs = torch.sigmoid(logits_per_image) # these are the probabilities
|
||||
>>> print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'")
|
||||
31.9% that image 0 is 'a photo of 2 cats'
|
||||
```
|
||||
|
||||
## SiglipConfig
|
||||
|
||||
[[autodoc]] SiglipConfig
|
||||
- from_text_vision_configs
|
||||
|
||||
## SiglipTextConfig
|
||||
|
||||
[[autodoc]] SiglipTextConfig
|
||||
|
||||
## SiglipVisionConfig
|
||||
|
||||
[[autodoc]] SiglipVisionConfig
|
||||
|
||||
## SiglipTokenizer
|
||||
|
||||
[[autodoc]] SiglipTokenizer
|
||||
- build_inputs_with_special_tokens
|
||||
- get_special_tokens_mask
|
||||
- create_token_type_ids_from_sequences
|
||||
- save_vocabulary
|
||||
|
||||
## SiglipImageProcessor
|
||||
|
||||
[[autodoc]] SiglipImageProcessor
|
||||
- preprocess
|
||||
|
||||
## SiglipProcessor
|
||||
|
||||
[[autodoc]] SiglipProcessor
|
||||
|
||||
## SiglipModel
|
||||
|
||||
[[autodoc]] SiglipModel
|
||||
- forward
|
||||
- get_text_features
|
||||
- get_image_features
|
||||
|
||||
## SiglipTextModel
|
||||
|
||||
[[autodoc]] SiglipTextModel
|
||||
- forward
|
||||
|
||||
## SiglipVisionModel
|
||||
|
||||
[[autodoc]] SiglipVisionModel
|
||||
- forward
|
||||
|
|
@ -762,6 +762,14 @@ _import_structure = {
|
|||
"models.segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig"],
|
||||
"models.sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"],
|
||||
"models.sew_d": ["SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWDConfig"],
|
||||
"models.siglip": [
|
||||
"SIGLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
|
||||
"SiglipConfig",
|
||||
"SiglipProcessor",
|
||||
"SiglipTextConfig",
|
||||
"SiglipTokenizer",
|
||||
"SiglipVisionConfig",
|
||||
],
|
||||
"models.speech_encoder_decoder": ["SpeechEncoderDecoderConfig"],
|
||||
"models.speech_to_text": [
|
||||
"SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
|
||||
|
|
@ -1290,6 +1298,7 @@ else:
|
|||
_import_structure["models.pvt"].extend(["PvtImageProcessor"])
|
||||
_import_structure["models.sam"].extend(["SamImageProcessor"])
|
||||
_import_structure["models.segformer"].extend(["SegformerFeatureExtractor", "SegformerImageProcessor"])
|
||||
_import_structure["models.siglip"].append("SiglipImageProcessor")
|
||||
_import_structure["models.swin2sr"].append("Swin2SRImageProcessor")
|
||||
_import_structure["models.tvlt"].append("TvltImageProcessor")
|
||||
_import_structure["models.tvp"].append("TvpImageProcessor")
|
||||
|
|
@ -3155,6 +3164,15 @@ else:
|
|||
"SEWDPreTrainedModel",
|
||||
]
|
||||
)
|
||||
_import_structure["models.siglip"].extend(
|
||||
[
|
||||
"SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
|
||||
"SiglipModel",
|
||||
"SiglipPreTrainedModel",
|
||||
"SiglipTextModel",
|
||||
"SiglipVisionModel",
|
||||
]
|
||||
)
|
||||
_import_structure["models.speech_encoder_decoder"].extend(["SpeechEncoderDecoderModel"])
|
||||
_import_structure["models.speech_to_text"].extend(
|
||||
[
|
||||
|
|
@ -5441,6 +5459,14 @@ if TYPE_CHECKING:
|
|||
)
|
||||
from .models.sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig
|
||||
from .models.sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig
|
||||
from .models.siglip import (
|
||||
SIGLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
|
||||
SiglipConfig,
|
||||
SiglipProcessor,
|
||||
SiglipTextConfig,
|
||||
SiglipTokenizer,
|
||||
SiglipVisionConfig,
|
||||
)
|
||||
from .models.speech_encoder_decoder import SpeechEncoderDecoderConfig
|
||||
from .models.speech_to_text import (
|
||||
SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
|
||||
|
|
@ -5966,6 +5992,7 @@ if TYPE_CHECKING:
|
|||
from .models.pvt import PvtImageProcessor
|
||||
from .models.sam import SamImageProcessor
|
||||
from .models.segformer import SegformerFeatureExtractor, SegformerImageProcessor
|
||||
from .models.siglip import SiglipImageProcessor
|
||||
from .models.swin2sr import Swin2SRImageProcessor
|
||||
from .models.tvlt import TvltImageProcessor
|
||||
from .models.tvp import TvpImageProcessor
|
||||
|
|
@ -7508,6 +7535,13 @@ if TYPE_CHECKING:
|
|||
SEWDModel,
|
||||
SEWDPreTrainedModel,
|
||||
)
|
||||
from .models.siglip import (
|
||||
SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
|
||||
SiglipModel,
|
||||
SiglipPreTrainedModel,
|
||||
SiglipTextModel,
|
||||
SiglipVisionModel,
|
||||
)
|
||||
from .models.speech_encoder_decoder import SpeechEncoderDecoderModel
|
||||
from .models.speech_to_text import (
|
||||
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
|
||||
|
|
|
|||
|
|
@ -193,6 +193,7 @@ from . import (
|
|||
segformer,
|
||||
sew,
|
||||
sew_d,
|
||||
siglip,
|
||||
speech_encoder_decoder,
|
||||
speech_to_text,
|
||||
speech_to_text_2,
|
||||
|
|
|
|||
|
|
@ -200,6 +200,8 @@ CONFIG_MAPPING_NAMES = OrderedDict(
|
|||
("segformer", "SegformerConfig"),
|
||||
("sew", "SEWConfig"),
|
||||
("sew-d", "SEWDConfig"),
|
||||
("siglip", "SiglipConfig"),
|
||||
("siglip_vision_model", "SiglipVisionConfig"),
|
||||
("speech-encoder-decoder", "SpeechEncoderDecoderConfig"),
|
||||
("speech_to_text", "Speech2TextConfig"),
|
||||
("speech_to_text_2", "Speech2Text2Config"),
|
||||
|
|
@ -419,6 +421,7 @@ CONFIG_ARCHIVE_MAP_MAPPING_NAMES = OrderedDict(
|
|||
("segformer", "SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("sew", "SEW_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("sew-d", "SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("siglip", "SIGLIP_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("speech_to_text", "SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("speech_to_text_2", "SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("speecht5", "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
|
|
@ -664,6 +667,8 @@ MODEL_NAMES_MAPPING = OrderedDict(
|
|||
("segformer", "SegFormer"),
|
||||
("sew", "SEW"),
|
||||
("sew-d", "SEW-D"),
|
||||
("siglip", "SigLIP"),
|
||||
("siglip_vision_model", "SiglipVisionModel"),
|
||||
("speech-encoder-decoder", "Speech Encoder decoder"),
|
||||
("speech_to_text", "Speech2Text"),
|
||||
("speech_to_text_2", "Speech2Text2"),
|
||||
|
|
@ -755,6 +760,7 @@ SPECIAL_MODEL_TYPE_TO_MODULE_NAME = OrderedDict(
|
|||
("maskformer-swin", "maskformer"),
|
||||
("xclip", "x_clip"),
|
||||
("clip_vision_model", "clip"),
|
||||
("siglip_vision_model", "siglip"),
|
||||
]
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -97,6 +97,7 @@ IMAGE_PROCESSOR_MAPPING_NAMES = OrderedDict(
|
|||
("resnet", "ConvNextImageProcessor"),
|
||||
("sam", "SamImageProcessor"),
|
||||
("segformer", "SegformerImageProcessor"),
|
||||
("siglip", "SiglipImageProcessor"),
|
||||
("swiftformer", "ViTImageProcessor"),
|
||||
("swin", "ViTImageProcessor"),
|
||||
("swin2sr", "Swin2SRImageProcessor"),
|
||||
|
|
|
|||
|
|
@ -193,6 +193,8 @@ MODEL_MAPPING_NAMES = OrderedDict(
|
|||
("segformer", "SegformerModel"),
|
||||
("sew", "SEWModel"),
|
||||
("sew-d", "SEWDModel"),
|
||||
("siglip", "SiglipModel"),
|
||||
("siglip_vision_model", "SiglipVisionModel"),
|
||||
("speech_to_text", "Speech2TextModel"),
|
||||
("speecht5", "SpeechT5Model"),
|
||||
("splinter", "SplinterModel"),
|
||||
|
|
@ -1102,6 +1104,7 @@ MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
|
|||
("chinese_clip", "ChineseCLIPModel"),
|
||||
("clip", "CLIPModel"),
|
||||
("clipseg", "CLIPSegModel"),
|
||||
("siglip", "SiglipModel"),
|
||||
]
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -77,6 +77,7 @@ PROCESSOR_MAPPING_NAMES = OrderedDict(
|
|||
("seamless_m4t", "SeamlessM4TProcessor"),
|
||||
("sew", "Wav2Vec2Processor"),
|
||||
("sew-d", "Wav2Vec2Processor"),
|
||||
("siglip", "SiglipProcessor"),
|
||||
("speech_to_text", "Speech2TextProcessor"),
|
||||
("speech_to_text_2", "Speech2Text2Processor"),
|
||||
("speecht5", "SpeechT5Processor"),
|
||||
|
|
|
|||
|
|
@ -372,6 +372,7 @@ else:
|
|||
"SeamlessM4TTokenizerFast" if is_tokenizers_available() else None,
|
||||
),
|
||||
),
|
||||
("siglip", ("SiglipTokenizer", None)),
|
||||
("speech_to_text", ("Speech2TextTokenizer" if is_sentencepiece_available() else None, None)),
|
||||
("speech_to_text_2", ("Speech2Text2Tokenizer", None)),
|
||||
("speecht5", ("SpeechT5Tokenizer" if is_sentencepiece_available() else None, None)),
|
||||
|
|
|
|||
|
|
@ -0,0 +1,94 @@
|
|||
# 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
|
||||
|
||||
from ...utils import (
|
||||
OptionalDependencyNotAvailable,
|
||||
_LazyModule,
|
||||
is_torch_available,
|
||||
is_vision_available,
|
||||
)
|
||||
|
||||
|
||||
_import_structure = {
|
||||
"configuration_siglip": [
|
||||
"SIGLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
|
||||
"SiglipConfig",
|
||||
"SiglipTextConfig",
|
||||
"SiglipVisionConfig",
|
||||
],
|
||||
"processing_siglip": ["SiglipProcessor"],
|
||||
"tokenization_siglip": ["SiglipTokenizer"],
|
||||
}
|
||||
|
||||
try:
|
||||
if not is_vision_available():
|
||||
raise OptionalDependencyNotAvailable()
|
||||
except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
_import_structure["image_processing_siglip"] = ["SiglipImageProcessor"]
|
||||
|
||||
try:
|
||||
if not is_torch_available():
|
||||
raise OptionalDependencyNotAvailable()
|
||||
except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
_import_structure["modeling_siglip"] = [
|
||||
"SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
|
||||
"SiglipModel",
|
||||
"SiglipPreTrainedModel",
|
||||
"SiglipTextModel",
|
||||
"SiglipVisionModel",
|
||||
]
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from .configuration_siglip import (
|
||||
SIGLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
|
||||
SiglipConfig,
|
||||
SiglipTextConfig,
|
||||
SiglipVisionConfig,
|
||||
)
|
||||
from .processing_siglip import SiglipProcessor
|
||||
from .tokenization_siglip import SiglipTokenizer
|
||||
|
||||
try:
|
||||
if not is_vision_available():
|
||||
raise OptionalDependencyNotAvailable()
|
||||
except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
from .image_processing_siglip import SiglipImageProcessor
|
||||
|
||||
try:
|
||||
if not is_torch_available():
|
||||
raise OptionalDependencyNotAvailable()
|
||||
except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
from .modeling_siglip import (
|
||||
SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
|
||||
SiglipModel,
|
||||
SiglipPreTrainedModel,
|
||||
SiglipTextModel,
|
||||
SiglipVisionModel,
|
||||
)
|
||||
|
||||
|
||||
else:
|
||||
import sys
|
||||
|
||||
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
|
||||
|
|
@ -0,0 +1,302 @@
|
|||
# coding=utf-8
|
||||
# Copyright 2024 The HuggingFace Inc. 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.
|
||||
""" Siglip model configuration"""
|
||||
|
||||
import os
|
||||
from typing import Union
|
||||
|
||||
from ...configuration_utils import PretrainedConfig
|
||||
from ...utils import logging
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
SIGLIP_PRETRAINED_CONFIG_ARCHIVE_MAP = {
|
||||
"google/siglip-base-patch16-224": "https://huggingface.co/google/siglip-base-patch16-224/resolve/main/config.json",
|
||||
}
|
||||
|
||||
|
||||
class SiglipTextConfig(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of a [`SiglipTextModel`]. It is used to instantiate a
|
||||
Siglip text encoder according to the specified arguments, defining the model architecture. Instantiating a
|
||||
configuration with the defaults will yield a similar configuration to that of the text encoder of the Siglip
|
||||
[google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture.
|
||||
|
||||
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
|
||||
|
||||
Args:
|
||||
vocab_size (`int`, *optional*, defaults to 32000):
|
||||
Vocabulary size of the Siglip text model. Defines the number of different tokens that can be represented by
|
||||
the `inputs_ids` passed when calling [`SiglipModel`].
|
||||
hidden_size (`int`, *optional*, defaults to 768):
|
||||
Dimensionality of the encoder layers and the pooler layer.
|
||||
intermediate_size (`int`, *optional*, defaults to 3072):
|
||||
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
|
||||
num_hidden_layers (`int`, *optional*, defaults to 12):
|
||||
Number of hidden layers in the Transformer encoder.
|
||||
num_attention_heads (`int`, *optional*, defaults to 12):
|
||||
Number of attention heads for each attention layer in the Transformer encoder.
|
||||
max_position_embeddings (`int`, *optional*, defaults to 64):
|
||||
The maximum sequence length that this model might ever be used with. Typically set this to something large
|
||||
just in case (e.g., 512 or 1024 or 2048).
|
||||
hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
|
||||
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
|
||||
`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
|
||||
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
|
||||
The epsilon used by the layer normalization layers.
|
||||
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||
The dropout ratio for the attention probabilities.
|
||||
pad_token_id (`int`, *optional*, defaults to 1):
|
||||
The id of the padding token in the vocabulary.
|
||||
bos_token_id (`int`, *optional*, defaults to 49406):
|
||||
The id of the beginning-of-sequence token in the vocabulary.
|
||||
eos_token_id (`int`, *optional*, defaults to 49407):
|
||||
The id of the end-of-sequence token in the vocabulary.
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
>>> from transformers import SiglipTextConfig, SiglipTextModel
|
||||
|
||||
>>> # Initializing a SiglipTextConfig with google/siglip-base-patch16-224 style configuration
|
||||
>>> configuration = SiglipTextConfig()
|
||||
|
||||
>>> # Initializing a SiglipTextModel (with random weights) from the google/siglip-base-patch16-224 style configuration
|
||||
>>> model = SiglipTextModel(configuration)
|
||||
|
||||
>>> # Accessing the model configuration
|
||||
>>> configuration = model.config
|
||||
```"""
|
||||
|
||||
model_type = "siglip_text_model"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_size=32000,
|
||||
hidden_size=768,
|
||||
intermediate_size=3072,
|
||||
num_hidden_layers=12,
|
||||
num_attention_heads=12,
|
||||
max_position_embeddings=64,
|
||||
hidden_act="gelu_pytorch_tanh",
|
||||
layer_norm_eps=1e-6,
|
||||
attention_dropout=0.0,
|
||||
# This differs from `CLIPTokenizer`'s default and from openai/siglip
|
||||
# See https://github.com/huggingface/transformers/pull/24773#issuecomment-1632287538
|
||||
pad_token_id=1,
|
||||
bos_token_id=49406,
|
||||
eos_token_id=49407,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
|
||||
|
||||
self.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.intermediate_size = intermediate_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.layer_norm_eps = layer_norm_eps
|
||||
self.hidden_act = hidden_act
|
||||
self.attention_dropout = attention_dropout
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
|
||||
cls._set_token_in_kwargs(kwargs)
|
||||
|
||||
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
|
||||
|
||||
# get the text config dict if we are loading from SiglipConfig
|
||||
if config_dict.get("model_type") == "siglip":
|
||||
config_dict = config_dict["text_config"]
|
||||
|
||||
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
|
||||
logger.warning(
|
||||
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
|
||||
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
|
||||
)
|
||||
|
||||
return cls.from_dict(config_dict, **kwargs)
|
||||
|
||||
|
||||
class SiglipVisionConfig(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of a [`SiglipVisionModel`]. It is used to instantiate a
|
||||
Siglip vision encoder according to the specified arguments, defining the model architecture. Instantiating a
|
||||
configuration with the defaults will yield a similar configuration to that of the vision encoder of the Siglip
|
||||
[google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture.
|
||||
|
||||
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
|
||||
|
||||
Args:
|
||||
hidden_size (`int`, *optional*, defaults to 768):
|
||||
Dimensionality of the encoder layers and the pooler layer.
|
||||
intermediate_size (`int`, *optional*, defaults to 3072):
|
||||
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
|
||||
num_hidden_layers (`int`, *optional*, defaults to 12):
|
||||
Number of hidden layers in the Transformer encoder.
|
||||
num_attention_heads (`int`, *optional*, defaults to 12):
|
||||
Number of attention heads for each attention layer in the Transformer encoder.
|
||||
num_channels (`int`, *optional*, defaults to 3):
|
||||
Number of channels in the input images.
|
||||
image_size (`int`, *optional*, defaults to 224):
|
||||
The size (resolution) of each image.
|
||||
patch_size (`int`, *optional*, defaults to 16):
|
||||
The size (resolution) of each patch.
|
||||
hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
|
||||
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
|
||||
`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
|
||||
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
|
||||
The epsilon used by the layer normalization layers.
|
||||
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||
The dropout ratio for the attention probabilities.
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
>>> from transformers import SiglipVisionConfig, SiglipVisionModel
|
||||
|
||||
>>> # Initializing a SiglipVisionConfig with google/siglip-base-patch16-224 style configuration
|
||||
>>> configuration = SiglipVisionConfig()
|
||||
|
||||
>>> # Initializing a SiglipVisionModel (with random weights) from the google/siglip-base-patch16-224 style configuration
|
||||
>>> model = SiglipVisionModel(configuration)
|
||||
|
||||
>>> # Accessing the model configuration
|
||||
>>> configuration = model.config
|
||||
```"""
|
||||
|
||||
model_type = "siglip_vision_model"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
hidden_size=768,
|
||||
intermediate_size=3072,
|
||||
num_hidden_layers=12,
|
||||
num_attention_heads=12,
|
||||
num_channels=3,
|
||||
image_size=224,
|
||||
patch_size=16,
|
||||
hidden_act="gelu_pytorch_tanh",
|
||||
layer_norm_eps=1e-6,
|
||||
attention_dropout=0.0,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.hidden_size = hidden_size
|
||||
self.intermediate_size = intermediate_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.num_channels = num_channels
|
||||
self.patch_size = patch_size
|
||||
self.image_size = image_size
|
||||
self.attention_dropout = attention_dropout
|
||||
self.layer_norm_eps = layer_norm_eps
|
||||
self.hidden_act = hidden_act
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
|
||||
cls._set_token_in_kwargs(kwargs)
|
||||
|
||||
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
|
||||
|
||||
# get the vision config dict if we are loading from SiglipConfig
|
||||
if config_dict.get("model_type") == "siglip":
|
||||
config_dict = config_dict["vision_config"]
|
||||
|
||||
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
|
||||
logger.warning(
|
||||
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
|
||||
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
|
||||
)
|
||||
|
||||
return cls.from_dict(config_dict, **kwargs)
|
||||
|
||||
|
||||
class SiglipConfig(PretrainedConfig):
|
||||
r"""
|
||||
[`SiglipConfig`] is the configuration class to store the configuration of a [`SiglipModel`]. It is used to
|
||||
instantiate a Siglip model according to the specified arguments, defining the text model and vision model configs.
|
||||
Instantiating a configuration with the defaults will yield a similar configuration to that of the Siglip
|
||||
[google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture.
|
||||
|
||||
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
|
||||
|
||||
Args:
|
||||
text_config (`dict`, *optional*):
|
||||
Dictionary of configuration options used to initialize [`SiglipTextConfig`].
|
||||
vision_config (`dict`, *optional*):
|
||||
Dictionary of configuration options used to initialize [`SiglipVisionConfig`].
|
||||
kwargs (*optional*):
|
||||
Dictionary of keyword arguments.
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
>>> from transformers import SiglipConfig, SiglipModel
|
||||
|
||||
>>> # Initializing a SiglipConfig with google/siglip-base-patch16-224 style configuration
|
||||
>>> configuration = SiglipConfig()
|
||||
|
||||
>>> # Initializing a SiglipModel (with random weights) from the google/siglip-base-patch16-224 style configuration
|
||||
>>> model = SiglipModel(configuration)
|
||||
|
||||
>>> # Accessing the model configuration
|
||||
>>> configuration = model.config
|
||||
|
||||
>>> # We can also initialize a SiglipConfig from a SiglipTextConfig and a SiglipVisionConfig
|
||||
>>> from transformers import SiglipTextConfig, SiglipVisionConfig
|
||||
|
||||
>>> # Initializing a SiglipText and SiglipVision configuration
|
||||
>>> config_text = SiglipTextConfig()
|
||||
>>> config_vision = SiglipVisionConfig()
|
||||
|
||||
>>> config = SiglipConfig.from_text_vision_configs(config_text, config_vision)
|
||||
```"""
|
||||
|
||||
model_type = "siglip"
|
||||
|
||||
def __init__(self, text_config=None, vision_config=None, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
if text_config is None:
|
||||
text_config = {}
|
||||
logger.info("`text_config` is `None`. Initializing the `SiglipTextConfig` with default values.")
|
||||
|
||||
if vision_config is None:
|
||||
vision_config = {}
|
||||
logger.info("`vision_config` is `None`. initializing the `SiglipVisionConfig` with default values.")
|
||||
|
||||
self.text_config = SiglipTextConfig(**text_config)
|
||||
self.vision_config = SiglipVisionConfig(**vision_config)
|
||||
|
||||
self.initializer_factor = 1.0
|
||||
|
||||
@classmethod
|
||||
def from_text_vision_configs(cls, text_config: SiglipTextConfig, vision_config: SiglipVisionConfig, **kwargs):
|
||||
r"""
|
||||
Instantiate a [`SiglipConfig`] (or a derived class) from siglip text model configuration and siglip vision
|
||||
model configuration.
|
||||
|
||||
Returns:
|
||||
[`SiglipConfig`]: An instance of a configuration object
|
||||
"""
|
||||
|
||||
return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)
|
||||
|
|
@ -0,0 +1,413 @@
|
|||
# coding=utf-8
|
||||
# Copyright 2024 The HuggingFace Inc. team.
|
||||
#
|
||||
# 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.
|
||||
"""Convert SigLIP checkpoints from the original repository.
|
||||
|
||||
URL: https://github.com/google-research/big_vision/tree/main
|
||||
"""
|
||||
|
||||
|
||||
import argparse
|
||||
import collections
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import requests
|
||||
import torch
|
||||
from huggingface_hub import hf_hub_download
|
||||
from numpy import load
|
||||
from PIL import Image
|
||||
|
||||
from transformers import SiglipConfig, SiglipImageProcessor, SiglipModel, SiglipProcessor, SiglipTokenizer
|
||||
from transformers.utils import logging
|
||||
|
||||
|
||||
logging.set_verbosity_info()
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
model_name_to_checkpoint = {
|
||||
# base checkpoints
|
||||
"siglip-base-patch16-224": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_224_63724782.npz",
|
||||
"siglip-base-patch16-256": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_256_60500360.npz",
|
||||
"siglip-base-patch16-384": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_384_68578854.npz",
|
||||
"siglip-base-patch16-512": "/Users/nielsrogge/Documents/SigLIP/webli_en_b16_512_68580893.npz",
|
||||
# large checkpoints
|
||||
"siglip-large-patch16-256": "/Users/nielsrogge/Documents/SigLIP/webli_en_l16_256_60552751.npz",
|
||||
"siglip-large-patch16-384": "/Users/nielsrogge/Documents/SigLIP/webli_en_l16_384_63634585.npz",
|
||||
# multilingual checkpoint
|
||||
"siglip-base-patch16-256-i18n": "/Users/nielsrogge/Documents/SigLIP/webli_i18n_b16_256_66117334.npz",
|
||||
# so400m checkpoints
|
||||
"siglip-so400m-patch14-384": "/Users/nielsrogge/Documents/SigLIP/webli_en_so400m_384_58765454.npz",
|
||||
}
|
||||
|
||||
model_name_to_image_size = {
|
||||
"siglip-base-patch16-224": 224,
|
||||
"siglip-base-patch16-256": 256,
|
||||
"siglip-base-patch16-384": 384,
|
||||
"siglip-base-patch16-512": 512,
|
||||
"siglip-large-patch16-256": 256,
|
||||
"siglip-large-patch16-384": 384,
|
||||
"siglip-base-patch16-256-i18n": 256,
|
||||
"siglip-so400m-patch14-384": 384,
|
||||
}
|
||||
|
||||
|
||||
def get_siglip_config(model_name):
|
||||
config = SiglipConfig()
|
||||
|
||||
vocab_size = 250000 if "i18n" in model_name else 32000
|
||||
image_size = model_name_to_image_size[model_name]
|
||||
patch_size = 16 if "patch16" in model_name else 14
|
||||
|
||||
# size of the architecture
|
||||
config.vision_config.image_size = image_size
|
||||
config.vision_config.patch_size = patch_size
|
||||
config.text_config.vocab_size = vocab_size
|
||||
|
||||
if "base" in model_name:
|
||||
pass
|
||||
elif "large" in model_name:
|
||||
config.text_config.hidden_size = 1024
|
||||
config.text_config.intermediate_size = 4096
|
||||
config.text_config.num_hidden_layers = 24
|
||||
config.text_config.num_attention_heads = 16
|
||||
config.vision_config.hidden_size = 1024
|
||||
config.vision_config.intermediate_size = 4096
|
||||
config.vision_config.num_hidden_layers = 24
|
||||
config.vision_config.num_attention_heads = 16
|
||||
elif "so400m" in model_name:
|
||||
config.text_config.hidden_size = 1152
|
||||
config.text_config.intermediate_size = 4304
|
||||
config.text_config.num_hidden_layers = 27
|
||||
config.text_config.num_attention_heads = 16
|
||||
config.vision_config.hidden_size = 1152
|
||||
config.vision_config.intermediate_size = 4304
|
||||
config.vision_config.num_hidden_layers = 27
|
||||
config.vision_config.num_attention_heads = 16
|
||||
else:
|
||||
raise ValueError("Model not supported")
|
||||
|
||||
return config
|
||||
|
||||
|
||||
def create_rename_keys(config):
|
||||
rename_keys = []
|
||||
# fmt: off
|
||||
|
||||
# vision encoder
|
||||
|
||||
rename_keys.append(("params/img/embedding/kernel", "vision_model.embeddings.patch_embedding.weight"))
|
||||
rename_keys.append(("params/img/embedding/bias", "vision_model.embeddings.patch_embedding.bias"))
|
||||
rename_keys.append(("params/img/pos_embedding", "vision_model.embeddings.position_embedding.weight"))
|
||||
|
||||
for i in range(config.vision_config.num_hidden_layers):
|
||||
rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_0/scale", f"vision_model.encoder.layers.{i}.layer_norm1.weight"))
|
||||
rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_0/bias", f"vision_model.encoder.layers.{i}.layer_norm1.bias"))
|
||||
rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_1/scale", f"vision_model.encoder.layers.{i}.layer_norm2.weight"))
|
||||
rename_keys.append((f"params/img/Transformer/encoderblock_{i}/LayerNorm_1/bias", f"vision_model.encoder.layers.{i}.layer_norm2.bias"))
|
||||
rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_0/kernel", f"vision_model.encoder.layers.{i}.mlp.fc1.weight"))
|
||||
rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_0/bias", f"vision_model.encoder.layers.{i}.mlp.fc1.bias"))
|
||||
rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_1/kernel", f"vision_model.encoder.layers.{i}.mlp.fc2.weight"))
|
||||
rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MlpBlock_0/Dense_1/bias", f"vision_model.encoder.layers.{i}.mlp.fc2.bias"))
|
||||
rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/key/kernel", f"vision_model.encoder.layers.{i}.self_attn.k_proj.weight"))
|
||||
rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/key/bias", f"vision_model.encoder.layers.{i}.self_attn.k_proj.bias"))
|
||||
rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/value/kernel", f"vision_model.encoder.layers.{i}.self_attn.v_proj.weight"))
|
||||
rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/value/bias", f"vision_model.encoder.layers.{i}.self_attn.v_proj.bias"))
|
||||
rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/query/kernel", f"vision_model.encoder.layers.{i}.self_attn.q_proj.weight"))
|
||||
rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/query/bias", f"vision_model.encoder.layers.{i}.self_attn.q_proj.bias"))
|
||||
rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/out/kernel", f"vision_model.encoder.layers.{i}.self_attn.out_proj.weight"))
|
||||
rename_keys.append((f"params/img/Transformer/encoderblock_{i}/MultiHeadDotProductAttention_0/out/bias", f"vision_model.encoder.layers.{i}.self_attn.out_proj.bias"))
|
||||
|
||||
rename_keys.append(("params/img/Transformer/encoder_norm/scale", "vision_model.post_layernorm.weight"))
|
||||
rename_keys.append(("params/img/Transformer/encoder_norm/bias", "vision_model.post_layernorm.bias"))
|
||||
|
||||
rename_keys.append(("params/img/MAPHead_0/probe", "vision_model.head.probe"))
|
||||
rename_keys.append(("params/img/MAPHead_0/LayerNorm_0/scale", "vision_model.head.layernorm.weight"))
|
||||
rename_keys.append(("params/img/MAPHead_0/LayerNorm_0/bias", "vision_model.head.layernorm.bias"))
|
||||
rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_0/kernel", "vision_model.head.mlp.fc1.weight"))
|
||||
rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_0/bias", "vision_model.head.mlp.fc1.bias"))
|
||||
rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_1/kernel", "vision_model.head.mlp.fc2.weight"))
|
||||
rename_keys.append(("params/img/MAPHead_0/MlpBlock_0/Dense_1/bias", "vision_model.head.mlp.fc2.bias"))
|
||||
rename_keys.append(("params/img/MAPHead_0/MultiHeadDotProductAttention_0/out/kernel", "vision_model.head.attention.out_proj.weight"))
|
||||
rename_keys.append(("params/img/MAPHead_0/MultiHeadDotProductAttention_0/out/bias", "vision_model.head.attention.out_proj.bias"))
|
||||
|
||||
# text encoder
|
||||
|
||||
rename_keys.append(("params/txt/Embed_0/embedding", "text_model.embeddings.token_embedding.weight"))
|
||||
rename_keys.append(("params/txt/pos_embedding", "text_model.embeddings.position_embedding.weight"))
|
||||
|
||||
for i in range(config.text_config.num_hidden_layers):
|
||||
rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_0/scale", f"text_model.encoder.layers.{i}.layer_norm1.weight"))
|
||||
rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_0/bias", f"text_model.encoder.layers.{i}.layer_norm1.bias"))
|
||||
rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_1/scale", f"text_model.encoder.layers.{i}.layer_norm2.weight"))
|
||||
rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/LayerNorm_1/bias", f"text_model.encoder.layers.{i}.layer_norm2.bias"))
|
||||
rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_0/kernel", f"text_model.encoder.layers.{i}.mlp.fc1.weight"))
|
||||
rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_0/bias", f"text_model.encoder.layers.{i}.mlp.fc1.bias"))
|
||||
rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_1/kernel", f"text_model.encoder.layers.{i}.mlp.fc2.weight"))
|
||||
rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MlpBlock_0/Dense_1/bias", f"text_model.encoder.layers.{i}.mlp.fc2.bias"))
|
||||
rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/key/kernel", f"text_model.encoder.layers.{i}.self_attn.k_proj.weight"))
|
||||
rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/key/bias", f"text_model.encoder.layers.{i}.self_attn.k_proj.bias"))
|
||||
rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/value/kernel", f"text_model.encoder.layers.{i}.self_attn.v_proj.weight"))
|
||||
rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/value/bias", f"text_model.encoder.layers.{i}.self_attn.v_proj.bias"))
|
||||
rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/query/kernel", f"text_model.encoder.layers.{i}.self_attn.q_proj.weight"))
|
||||
rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/query/bias", f"text_model.encoder.layers.{i}.self_attn.q_proj.bias"))
|
||||
rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/out/kernel", f"text_model.encoder.layers.{i}.self_attn.out_proj.weight"))
|
||||
rename_keys.append((f"params/txt/Encoder_0/encoderblock_{i}/MultiHeadDotProductAttention_0/out/bias", f"text_model.encoder.layers.{i}.self_attn.out_proj.bias"))
|
||||
|
||||
rename_keys.append(("params/txt/Encoder_0/encoder_norm/scale", "text_model.final_layer_norm.weight"))
|
||||
rename_keys.append(("params/txt/Encoder_0/encoder_norm/bias", "text_model.final_layer_norm.bias"))
|
||||
rename_keys.append(("params/txt/head/kernel", "text_model.head.weight"))
|
||||
rename_keys.append(("params/txt/head/bias", "text_model.head.bias"))
|
||||
|
||||
# learned temperature and bias
|
||||
rename_keys.append(("params/t", "logit_scale"))
|
||||
rename_keys.append(("params/b", "logit_bias"))
|
||||
|
||||
# fmt: on
|
||||
return rename_keys
|
||||
|
||||
|
||||
def rename_key(dct, old, new, config):
|
||||
val = dct.pop(old)
|
||||
|
||||
if ("out_proj" in new or "v_proj" in new or "k_proj" in new or "q_proj" in new) and "vision" in new:
|
||||
val = val.reshape(-1, config.vision_config.hidden_size)
|
||||
if ("out_proj" in new or "v_proj" in new or "k_proj" in new or "q_proj" in new) and "text" in new:
|
||||
val = val.reshape(-1, config.text_config.hidden_size)
|
||||
|
||||
if "patch_embedding.weight" in new:
|
||||
val = val.transpose(3, 2, 0, 1)
|
||||
elif new.endswith("weight") and "position_embedding" not in new and "token_embedding" not in new:
|
||||
val = val.T
|
||||
|
||||
if "position_embedding" in new and "vision" in new:
|
||||
val = val.reshape(-1, config.vision_config.hidden_size)
|
||||
if "position_embedding" in new and "text" in new:
|
||||
val = val.reshape(-1, config.text_config.hidden_size)
|
||||
|
||||
if new.endswith("bias"):
|
||||
val = val.reshape(-1)
|
||||
|
||||
dct[new] = torch.from_numpy(val)
|
||||
|
||||
|
||||
def read_in_q_k_v_head(state_dict, config):
|
||||
# read in individual input projection layers
|
||||
key_proj_weight = (
|
||||
state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/key/kernel")
|
||||
.reshape(-1, config.vision_config.hidden_size)
|
||||
.T
|
||||
)
|
||||
key_proj_bias = state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/key/bias").reshape(-1)
|
||||
value_proj_weight = (
|
||||
state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/value/kernel")
|
||||
.reshape(-1, config.vision_config.hidden_size)
|
||||
.T
|
||||
)
|
||||
value_proj_bias = state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/value/bias").reshape(-1)
|
||||
query_proj_weight = (
|
||||
state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/query/kernel")
|
||||
.reshape(-1, config.vision_config.hidden_size)
|
||||
.T
|
||||
)
|
||||
query_proj_bias = state_dict.pop("params/img/MAPHead_0/MultiHeadDotProductAttention_0/query/bias").reshape(-1)
|
||||
|
||||
# next, add them to the state dict as a single matrix + vector
|
||||
state_dict["vision_model.head.attention.in_proj_weight"] = torch.from_numpy(
|
||||
np.concatenate([query_proj_weight, key_proj_weight, value_proj_weight], axis=0)
|
||||
)
|
||||
state_dict["vision_model.head.attention.in_proj_bias"] = torch.from_numpy(
|
||||
np.concatenate([query_proj_bias, key_proj_bias, value_proj_bias], axis=0)
|
||||
)
|
||||
|
||||
|
||||
# We will verify our results on an image of cute cats
|
||||
def prepare_img():
|
||||
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
||||
image = Image.open(requests.get(url, stream=True).raw)
|
||||
return image
|
||||
|
||||
|
||||
def flatten_nested_dict(params, parent_key="", sep="/"):
|
||||
items = []
|
||||
|
||||
for k, v in params.items():
|
||||
new_key = parent_key + sep + k if parent_key else k
|
||||
|
||||
if isinstance(v, collections.abc.MutableMapping):
|
||||
items.extend(flatten_nested_dict(v, new_key, sep=sep).items())
|
||||
else:
|
||||
items.append((new_key, v))
|
||||
return dict(items)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def convert_siglip_checkpoint(model_name, pytorch_dump_folder_path, verify_logits=True, push_to_hub=False):
|
||||
"""
|
||||
Copy/paste/tweak model's weights to our SigLIP structure.
|
||||
"""
|
||||
|
||||
# define default SigLIP configuration
|
||||
config = get_siglip_config(model_name)
|
||||
|
||||
# get checkpoint
|
||||
checkpoint = model_name_to_checkpoint[model_name]
|
||||
|
||||
# get vocab file
|
||||
if "i18n" in model_name:
|
||||
vocab_file = "/Users/nielsrogge/Documents/SigLIP/multilingual_vocab/sentencepiece.model"
|
||||
else:
|
||||
vocab_file = "/Users/nielsrogge/Documents/SigLIP/english_vocab/sentencepiece.model"
|
||||
|
||||
# load original state dict
|
||||
data = load(checkpoint)
|
||||
state_dict = flatten_nested_dict(data)
|
||||
|
||||
# remove and rename some keys
|
||||
rename_keys = create_rename_keys(config)
|
||||
for src, dest in rename_keys:
|
||||
rename_key(state_dict, src, dest, config)
|
||||
|
||||
# qkv matrices of attention pooling head need special treatment
|
||||
read_in_q_k_v_head(state_dict, config)
|
||||
|
||||
# load HuggingFace model
|
||||
model = SiglipModel(config).eval()
|
||||
model.load_state_dict(state_dict)
|
||||
|
||||
# create processor
|
||||
# important: make tokenizer not return attention_mask since original one doesn't require it
|
||||
image_size = config.vision_config.image_size
|
||||
size = {"height": image_size, "width": image_size}
|
||||
image_processor = SiglipImageProcessor(size=size)
|
||||
tokenizer = SiglipTokenizer(vocab_file=vocab_file, model_input_names=["input_ids"])
|
||||
processor = SiglipProcessor(image_processor=image_processor, tokenizer=tokenizer)
|
||||
|
||||
# verify on dummy images and texts
|
||||
url_1 = "https://cdn.openai.com/multimodal-neurons/assets/apple/apple-ipod.jpg"
|
||||
image_1 = Image.open(requests.get(url_1, stream=True).raw).convert("RGB")
|
||||
url_2 = "https://cdn.openai.com/multimodal-neurons/assets/apple/apple-blank.jpg"
|
||||
image_2 = Image.open(requests.get(url_2, stream=True).raw).convert("RGB")
|
||||
texts = ["an apple", "a picture of an apple"]
|
||||
|
||||
inputs = processor(images=[image_1, image_2], text=texts, return_tensors="pt", padding="max_length")
|
||||
|
||||
# verify input_ids against original ones
|
||||
if image_size == 224:
|
||||
filename = "siglip_pixel_values.pt"
|
||||
elif image_size == 256:
|
||||
filename = "siglip_pixel_values_256.pt"
|
||||
elif image_size == 384:
|
||||
filename = "siglip_pixel_values_384.pt"
|
||||
elif image_size == 512:
|
||||
filename = "siglip_pixel_values_512.pt"
|
||||
else:
|
||||
raise ValueError("Image size not supported")
|
||||
|
||||
filepath = hf_hub_download(repo_id="nielsr/test-image", filename=filename, repo_type="dataset")
|
||||
original_pixel_values = torch.load(filepath)
|
||||
filepath = hf_hub_download(repo_id="nielsr/test-image", filename="siglip_input_ids.pt", repo_type="dataset")
|
||||
original_input_ids = torch.load(filepath)
|
||||
|
||||
if "i18n" not in model_name:
|
||||
assert inputs.input_ids.tolist() == original_input_ids.tolist()
|
||||
|
||||
print("Mean of original pixel values:", original_pixel_values.mean())
|
||||
print("Mean of new pixel values:", inputs.pixel_values.mean())
|
||||
|
||||
# note: we're testing with original pixel values here since we don't have exact pixel values
|
||||
with torch.no_grad():
|
||||
outputs = model(input_ids=inputs.input_ids, pixel_values=original_pixel_values)
|
||||
|
||||
# with torch.no_grad():
|
||||
# outputs = model(input_ids=inputs.input_ids, pixel_values=inputs.pixel_values)
|
||||
|
||||
print(outputs.logits_per_image[:3, :3])
|
||||
|
||||
probs = torch.sigmoid(outputs.logits_per_image) # these are the probabilities
|
||||
print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'")
|
||||
print(f"{probs[0][1]:.1%} that image 0 is '{texts[1]}'")
|
||||
|
||||
if verify_logits:
|
||||
if model_name == "siglip-base-patch16-224":
|
||||
expected_slice = torch.tensor(
|
||||
[[-2.9621, -2.1672], [-0.2713, 0.2910]],
|
||||
)
|
||||
elif model_name == "siglip-base-patch16-256":
|
||||
expected_slice = torch.tensor(
|
||||
[[-3.1146, -1.9894], [-0.7312, 0.6387]],
|
||||
)
|
||||
elif model_name == "siglip-base-patch16-384":
|
||||
expected_slice = torch.tensor(
|
||||
[[-2.8098, -2.1891], [-0.4242, 0.4102]],
|
||||
)
|
||||
elif model_name == "siglip-base-patch16-512":
|
||||
expected_slice = torch.tensor(
|
||||
[[-2.7899, -2.2668], [-0.4295, -0.0735]],
|
||||
)
|
||||
elif model_name == "siglip-large-patch16-256":
|
||||
expected_slice = torch.tensor(
|
||||
[[-1.5827, -0.5801], [-0.9153, 0.1363]],
|
||||
)
|
||||
elif model_name == "siglip-large-patch16-384":
|
||||
expected_slice = torch.tensor(
|
||||
[[-2.1523, -0.2899], [-0.2959, 0.7884]],
|
||||
)
|
||||
elif model_name == "siglip-so400m-patch14-384":
|
||||
expected_slice = torch.tensor([[-1.2441, -0.6649], [-0.7060, 0.7374]])
|
||||
elif model_name == "siglip-base-patch16-256-i18n":
|
||||
expected_slice = torch.tensor(
|
||||
[[-0.9064, 0.1073], [-0.0299, 0.5304]],
|
||||
)
|
||||
|
||||
assert torch.allclose(outputs.logits_per_image[:3, :3], expected_slice, atol=1e-4)
|
||||
print("Looks ok!")
|
||||
|
||||
if pytorch_dump_folder_path is not None:
|
||||
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
|
||||
print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
|
||||
model.save_pretrained(pytorch_dump_folder_path)
|
||||
print(f"Saving processor to {pytorch_dump_folder_path}")
|
||||
processor.save_pretrained(pytorch_dump_folder_path)
|
||||
|
||||
if push_to_hub:
|
||||
model.push_to_hub(f"nielsr/{model_name}")
|
||||
processor.push_to_hub(f"nielsr/{model_name}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
# Required parameters
|
||||
parser.add_argument(
|
||||
"--model_name",
|
||||
default="siglip-base-patch16-224",
|
||||
type=str,
|
||||
choices=model_name_to_checkpoint.keys(),
|
||||
help="Name of the model you'd like to convert.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--verify_logits",
|
||||
action="store_false",
|
||||
help="Whether to verify logits against the original implementation.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
convert_siglip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.verify_logits, args.push_to_hub)
|
||||
|
|
@ -0,0 +1,225 @@
|
|||
# coding=utf-8
|
||||
# Copyright 2024 The HuggingFace Inc. 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 SigLIP."""
|
||||
|
||||
from typing import Dict, List, Optional, Union
|
||||
|
||||
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
|
||||
from ...image_transforms import (
|
||||
resize,
|
||||
to_channel_dimension_format,
|
||||
)
|
||||
from ...image_utils import (
|
||||
IMAGENET_STANDARD_MEAN,
|
||||
IMAGENET_STANDARD_STD,
|
||||
ChannelDimension,
|
||||
ImageInput,
|
||||
PILImageResampling,
|
||||
infer_channel_dimension_format,
|
||||
is_scaled_image,
|
||||
make_list_of_images,
|
||||
to_numpy_array,
|
||||
valid_images,
|
||||
)
|
||||
from ...utils import TensorType, is_vision_available, logging
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
if is_vision_available():
|
||||
import PIL
|
||||
|
||||
|
||||
class SiglipImageProcessor(BaseImageProcessor):
|
||||
r"""
|
||||
Constructs a SigLIP image processor.
|
||||
|
||||
Args:
|
||||
do_resize (`bool`, *optional*, defaults to `True`):
|
||||
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
|
||||
`do_resize` in the `preprocess` method.
|
||||
size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`):
|
||||
Size of the image after resizing. Can be overridden by `size` in the `preprocess` method.
|
||||
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
|
||||
Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
|
||||
do_rescale (`bool`, *optional*, defaults to `True`):
|
||||
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden 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 overridden by `rescale_factor` in the `preprocess`
|
||||
method.
|
||||
do_normalize (`bool`, *optional*, defaults to `True`):
|
||||
Whether to normalize the image by the specified mean and standard deviation. Can be overridden by
|
||||
`do_normalize` in the `preprocess` method.
|
||||
image_mean (`float` or `List[float]`, *optional*, defaults to `[0.5, 0.5, 0.5]`):
|
||||
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
|
||||
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
|
||||
image_std (`float` or `List[float]`, *optional*, defaults to `[0.5, 0.5, 0.5]`):
|
||||
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
|
||||
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
|
||||
Can be overridden by the `image_std` parameter in the `preprocess` method.
|
||||
"""
|
||||
|
||||
model_input_names = ["pixel_values"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
do_resize: bool = True,
|
||||
size: Dict[str, int] = None,
|
||||
resample: PILImageResampling = PILImageResampling.BICUBIC,
|
||||
do_rescale: bool = True,
|
||||
rescale_factor: Union[int, float] = 1 / 255,
|
||||
do_normalize: bool = True,
|
||||
image_mean: Optional[Union[float, List[float]]] = None,
|
||||
image_std: Optional[Union[float, List[float]]] = None,
|
||||
**kwargs,
|
||||
) -> None:
|
||||
super().__init__(**kwargs)
|
||||
size = size if size is not None else {"height": 224, "width": 224}
|
||||
image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
|
||||
image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
|
||||
|
||||
self.do_resize = do_resize
|
||||
self.size = size
|
||||
self.resample = resample
|
||||
self.do_rescale = do_rescale
|
||||
self.rescale_factor = rescale_factor
|
||||
self.do_normalize = do_normalize
|
||||
self.image_mean = image_mean
|
||||
self.image_std = image_std
|
||||
|
||||
def preprocess(
|
||||
self,
|
||||
images: ImageInput,
|
||||
do_resize: bool = None,
|
||||
size: Dict[str, int] = None,
|
||||
resample: PILImageResampling = None,
|
||||
do_rescale: bool = None,
|
||||
rescale_factor: float = None,
|
||||
do_normalize: bool = None,
|
||||
image_mean: Optional[Union[float, List[float]]] = None,
|
||||
image_std: Optional[Union[float, List[float]]] = None,
|
||||
return_tensors: Optional[Union[str, TensorType]] = None,
|
||||
data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
|
||||
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
**kwargs,
|
||||
) -> PIL.Image.Image:
|
||||
"""
|
||||
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 image after resizing.
|
||||
resample (`int`, *optional*, defaults to `self.resample`):
|
||||
Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. 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.
|
||||
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
|
||||
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
|
||||
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
|
||||
Whether to normalize the image.
|
||||
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
|
||||
Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
|
||||
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
|
||||
Image standard deviation to use for normalization. Only has an effect if `do_normalize` 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
|
||||
size = size if size is not None else self.size
|
||||
size = get_size_dict(size, param_name="size", default_to_square=False)
|
||||
resample = resample if resample is not None else self.resample
|
||||
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
|
||||
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
|
||||
image_mean = image_mean if image_mean is not None else self.image_mean
|
||||
image_std = image_std if image_std is not None else self.image_std
|
||||
|
||||
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:
|
||||
height, width = size["height"], size["width"]
|
||||
images = [
|
||||
resize(image=image, size=(height, width), resample=resample, 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 do_normalize:
|
||||
images = [
|
||||
self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
|
||||
for image in images
|
||||
]
|
||||
|
||||
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)
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,143 @@
|
|||
# coding=utf-8
|
||||
# Copyright 2024 The HuggingFace Inc. team.
|
||||
#
|
||||
# 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/Text processor class for SigLIP.
|
||||
"""
|
||||
|
||||
from typing import List, Optional, Union
|
||||
|
||||
from ...feature_extraction_utils import BatchFeature
|
||||
from ...image_utils import ImageInput
|
||||
from ...processing_utils import ProcessorMixin
|
||||
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
|
||||
from ...utils import TensorType
|
||||
|
||||
|
||||
class SiglipProcessor(ProcessorMixin):
|
||||
r"""
|
||||
Constructs a Siglip processor which wraps a Siglip image processor and a Siglip tokenizer into a single processor.
|
||||
|
||||
[`SiglipProcessor`] offers all the functionalities of [`SiglipImageProcessor`] and [`SiglipTokenizer`]. See the
|
||||
[`~SiglipProcessor.__call__`] and [`~SiglipProcessor.decode`] for more information.
|
||||
|
||||
Args:
|
||||
image_processor ([`SiglipImageProcessor`]):
|
||||
The image processor is a required input.
|
||||
tokenizer ([`SiglipTokenizer`]):
|
||||
The tokenizer is a required input.
|
||||
"""
|
||||
|
||||
attributes = ["image_processor", "tokenizer"]
|
||||
image_processor_class = "SiglipImageProcessor"
|
||||
tokenizer_class = "SiglipTokenizer"
|
||||
|
||||
def __init__(self, image_processor, tokenizer):
|
||||
super().__init__(image_processor, tokenizer)
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
|
||||
images: ImageInput = None,
|
||||
padding: Union[bool, str, PaddingStrategy] = "max_length",
|
||||
truncation: Union[bool, str, TruncationStrategy] = None,
|
||||
max_length=None,
|
||||
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
|
||||
) -> BatchFeature:
|
||||
"""
|
||||
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
|
||||
and `kwargs` arguments to SiglipTokenizer's [`~SiglipTokenizer.__call__`] if `text` is not `None` to encode
|
||||
the text. To prepare the image(s), this method forwards the `images` argument to
|
||||
SiglipImageProcessor's [`~SiglipImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
|
||||
of the above two methods for more information.
|
||||
|
||||
Args:
|
||||
text (`str`, `List[str]`, `List[List[str]]`):
|
||||
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
|
||||
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
|
||||
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
|
||||
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
|
||||
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
|
||||
tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
|
||||
number of channels, H and W are image height and width.
|
||||
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `max_length`):
|
||||
Select a strategy to pad the returned sequences (according to the model's padding side and padding
|
||||
index) among:
|
||||
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
|
||||
sequence if provided).
|
||||
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
|
||||
acceptable input length for the model if that argument is not provided.
|
||||
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
|
||||
lengths).
|
||||
max_length (`int`, *optional*):
|
||||
Maximum length of the returned list and optionally padding length (see above).
|
||||
truncation (`bool`, *optional*):
|
||||
Activates truncation to cut input sequences longer than `max_length` to `max_length`.
|
||||
return_tensors (`str` or [`~utils.TensorType`], *optional*):
|
||||
If set, will return tensors of a particular framework. Acceptable values are:
|
||||
|
||||
- `'tf'`: Return TensorFlow `tf.constant` objects.
|
||||
- `'pt'`: Return PyTorch `torch.Tensor` objects.
|
||||
- `'np'`: Return NumPy `np.ndarray` objects.
|
||||
- `'jax'`: Return JAX `jnp.ndarray` objects.
|
||||
|
||||
Returns:
|
||||
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
|
||||
|
||||
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
|
||||
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
|
||||
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
|
||||
`None`).
|
||||
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
|
||||
"""
|
||||
|
||||
if text is None and images is None:
|
||||
raise ValueError("You have to specify either text or images. Both cannot be none.")
|
||||
|
||||
if text is not None:
|
||||
encoding = self.tokenizer(
|
||||
text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length
|
||||
)
|
||||
|
||||
if images is not None:
|
||||
image_features = self.image_processor(images, return_tensors=return_tensors)
|
||||
|
||||
if text is not None and images is not None:
|
||||
encoding["pixel_values"] = image_features.pixel_values
|
||||
return encoding
|
||||
elif text is not None:
|
||||
return encoding
|
||||
else:
|
||||
return BatchFeature(data=dict(**image_features), tensor_type=return_tensors)
|
||||
|
||||
def decode(self, *args, **kwargs):
|
||||
"""
|
||||
This method forwards all its arguments to SiglipTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
|
||||
the docstring of this method for more information.
|
||||
"""
|
||||
return self.tokenizer.decode(*args, **kwargs)
|
||||
|
||||
def batch_decode(self, *args, **kwargs):
|
||||
"""
|
||||
This method forwards all its arguments to SiglipTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
|
||||
refer to the docstring of this method for more information.
|
||||
"""
|
||||
return self.tokenizer.batch_decode(*args, **kwargs)
|
||||
|
||||
@property
|
||||
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names with CLIP->Siglip, T5->Siglip
|
||||
def model_input_names(self):
|
||||
tokenizer_input_names = self.tokenizer.model_input_names
|
||||
image_processor_input_names = self.image_processor.model_input_names
|
||||
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
|
||||
|
|
@ -0,0 +1,389 @@
|
|||
# coding=utf-8
|
||||
# Copyright 2024 The HuggingFace Inc. team.
|
||||
#
|
||||
# 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.
|
||||
""" Tokenization class for SigLIP model."""
|
||||
|
||||
import os
|
||||
import re
|
||||
import string
|
||||
import warnings
|
||||
from shutil import copyfile
|
||||
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
|
||||
|
||||
import sentencepiece as spm
|
||||
|
||||
from ...convert_slow_tokenizer import import_protobuf
|
||||
from ...tokenization_utils import PreTrainedTokenizer
|
||||
from ...tokenization_utils_base import AddedToken
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from ...tokenization_utils_base import TextInput
|
||||
from ...utils import logging, requires_backends
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}
|
||||
|
||||
PRETRAINED_VOCAB_FILES_MAP = {
|
||||
"vocab_file": {
|
||||
"google/siglip-base-patch16-224": "https://huggingface.co/google/siglip-base-patch16-224/resolve/main/spiece.model",
|
||||
}
|
||||
}
|
||||
|
||||
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
|
||||
"google/siglip-base-patch16-224": 256,
|
||||
}
|
||||
|
||||
SPIECE_UNDERLINE = "▁"
|
||||
|
||||
|
||||
class SiglipTokenizer(PreTrainedTokenizer):
|
||||
"""
|
||||
Construct a Siglip tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
|
||||
|
||||
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
|
||||
this superclass for more information regarding those methods.
|
||||
|
||||
Args:
|
||||
vocab_file (`str`):
|
||||
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
|
||||
contains the vocabulary necessary to instantiate a tokenizer.
|
||||
eos_token (`str`, *optional*, defaults to `"</s>"`):
|
||||
The end of sequence token.
|
||||
unk_token (`str`, *optional*, defaults to `"<unk>"`):
|
||||
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
|
||||
token instead.
|
||||
pad_token (`str`, *optional*, defaults to `"</s>"`):
|
||||
The token used for padding, for example when batching sequences of different lengths.
|
||||
additional_special_tokens (`List[str]`, *optional*):
|
||||
Additional special tokens used by the tokenizer.
|
||||
sp_model_kwargs (`dict`, *optional*):
|
||||
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
|
||||
SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
|
||||
to set:
|
||||
|
||||
- `enable_sampling`: Enable subword regularization.
|
||||
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
|
||||
|
||||
- `nbest_size = {0,1}`: No sampling is performed.
|
||||
- `nbest_size > 1`: samples from the nbest_size results.
|
||||
- `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
|
||||
using forward-filtering-and-backward-sampling algorithm.
|
||||
|
||||
- `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
|
||||
BPE-dropout.
|
||||
model_max_length (`int`, *optional*, defaults to 64):
|
||||
The maximum length (in number of tokens) for model inputs.
|
||||
do_lower_case (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not to lowercase the input when tokenizing.
|
||||
"""
|
||||
|
||||
vocab_files_names = VOCAB_FILES_NAMES
|
||||
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
|
||||
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
|
||||
model_input_names = ["input_ids", "attention_mask"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_file,
|
||||
eos_token="</s>",
|
||||
unk_token="<unk>",
|
||||
pad_token="</s>",
|
||||
additional_special_tokens=None,
|
||||
sp_model_kwargs: Optional[Dict[str, Any]] = None,
|
||||
model_max_length=64,
|
||||
do_lower_case=True,
|
||||
**kwargs,
|
||||
) -> None:
|
||||
requires_backends(self, "protobuf")
|
||||
|
||||
pad_token = (
|
||||
AddedToken(pad_token, rstrip=True, lstrip=True, normalized=False, special=True)
|
||||
if isinstance(pad_token, str)
|
||||
else pad_token
|
||||
)
|
||||
unk_token = (
|
||||
AddedToken(unk_token, rstrip=True, lstrip=True, normalized=False, special=True)
|
||||
if isinstance(unk_token, str)
|
||||
else unk_token
|
||||
)
|
||||
eos_token = (
|
||||
AddedToken(eos_token, rstrip=True, lstrip=True, normalized=False, special=True)
|
||||
if isinstance(eos_token, str)
|
||||
else eos_token
|
||||
)
|
||||
|
||||
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
|
||||
|
||||
self.do_lower_case = do_lower_case
|
||||
self.vocab_file = vocab_file
|
||||
|
||||
self.sp_model = self.get_spm_processor()
|
||||
self.vocab_file = vocab_file
|
||||
|
||||
super().__init__(
|
||||
eos_token=eos_token,
|
||||
unk_token=unk_token,
|
||||
pad_token=pad_token,
|
||||
additional_special_tokens=additional_special_tokens,
|
||||
sp_model_kwargs=self.sp_model_kwargs,
|
||||
model_max_length=model_max_length,
|
||||
do_lower_case=do_lower_case,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
def get_spm_processor(self):
|
||||
tokenizer = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
||||
with open(self.vocab_file, "rb") as f:
|
||||
sp_model = f.read()
|
||||
model_pb2 = import_protobuf()
|
||||
model = model_pb2.ModelProto.FromString(sp_model)
|
||||
normalizer_spec = model_pb2.NormalizerSpec()
|
||||
normalizer_spec.add_dummy_prefix = False
|
||||
model.normalizer_spec.MergeFrom(normalizer_spec)
|
||||
sp_model = model.SerializeToString()
|
||||
tokenizer.LoadFromSerializedProto(sp_model)
|
||||
return tokenizer
|
||||
|
||||
@property
|
||||
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.vocab_size
|
||||
def vocab_size(self):
|
||||
return self.sp_model.get_piece_size()
|
||||
|
||||
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_vocab
|
||||
def get_vocab(self):
|
||||
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
|
||||
vocab.update(self.added_tokens_encoder)
|
||||
return vocab
|
||||
|
||||
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.get_special_tokens_mask
|
||||
def get_special_tokens_mask(
|
||||
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
|
||||
) -> List[int]:
|
||||
"""
|
||||
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
|
||||
special tokens using the tokenizer `prepare_for_model` method.
|
||||
|
||||
Args:
|
||||
token_ids_0 (`List[int]`):
|
||||
List of IDs.
|
||||
token_ids_1 (`List[int]`, *optional*):
|
||||
Optional second list of IDs for sequence pairs.
|
||||
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
|
||||
Whether or not the token list is already formatted with special tokens for the model.
|
||||
|
||||
Returns:
|
||||
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
|
||||
"""
|
||||
if already_has_special_tokens:
|
||||
return super().get_special_tokens_mask(
|
||||
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
|
||||
)
|
||||
|
||||
# normal case: some special tokens
|
||||
if token_ids_1 is None:
|
||||
return ([0] * len(token_ids_0)) + [1]
|
||||
return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
|
||||
|
||||
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._add_eos_if_not_present
|
||||
def _add_eos_if_not_present(self, token_ids: List[int]) -> List[int]:
|
||||
"""Do not add eos again if user already added it."""
|
||||
if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id:
|
||||
warnings.warn(
|
||||
f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
|
||||
" eos tokens being added."
|
||||
)
|
||||
return token_ids
|
||||
else:
|
||||
return token_ids + [self.eos_token_id]
|
||||
|
||||
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.create_token_type_ids_from_sequences
|
||||
def create_token_type_ids_from_sequences(
|
||||
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
||||
) -> List[int]:
|
||||
"""
|
||||
Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
|
||||
use of token type ids, therefore a list of zeros is returned.
|
||||
|
||||
Args:
|
||||
token_ids_0 (`List[int]`):
|
||||
List of IDs.
|
||||
token_ids_1 (`List[int]`, *optional*):
|
||||
Optional second list of IDs for sequence pairs.
|
||||
|
||||
Returns:
|
||||
`List[int]`: List of zeros.
|
||||
"""
|
||||
eos = [self.eos_token_id]
|
||||
|
||||
if token_ids_1 is None:
|
||||
return len(token_ids_0 + eos) * [0]
|
||||
return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
|
||||
|
||||
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.build_inputs_with_special_tokens
|
||||
def build_inputs_with_special_tokens(
|
||||
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
||||
) -> List[int]:
|
||||
"""
|
||||
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
|
||||
adding special tokens. A sequence has the following format:
|
||||
|
||||
- single sequence: `X </s>`
|
||||
- pair of sequences: `A </s> B </s>`
|
||||
|
||||
Args:
|
||||
token_ids_0 (`List[int]`):
|
||||
List of IDs to which the special tokens will be added.
|
||||
token_ids_1 (`List[int]`, *optional*):
|
||||
Optional second list of IDs for sequence pairs.
|
||||
|
||||
Returns:
|
||||
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
|
||||
"""
|
||||
token_ids_0 = self._add_eos_if_not_present(token_ids_0)
|
||||
if token_ids_1 is None:
|
||||
return token_ids_0
|
||||
else:
|
||||
token_ids_1 = self._add_eos_if_not_present(token_ids_1)
|
||||
return token_ids_0 + token_ids_1
|
||||
|
||||
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.__getstate__
|
||||
def __getstate__(self):
|
||||
state = self.__dict__.copy()
|
||||
state["sp_model"] = None
|
||||
return state
|
||||
|
||||
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.__setstate__
|
||||
def __setstate__(self, d):
|
||||
self.__dict__ = d
|
||||
|
||||
# for backward compatibility
|
||||
if not hasattr(self, "sp_model_kwargs"):
|
||||
self.sp_model_kwargs = {}
|
||||
|
||||
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
||||
self.sp_model.Load(self.vocab_file)
|
||||
|
||||
def remove_punctuation(self, text: str) -> str:
|
||||
return text.translate(str.maketrans("", "", string.punctuation))
|
||||
|
||||
# source: https://github.com/google-research/big_vision/blob/3b8e5ab6ad4f96e32b32826f9e1b8fd277914f9c/big_vision/evaluators/proj/image_text/prompt_engineering.py#L94
|
||||
def canonicalize_text(self, text, *, keep_punctuation_exact_string=None):
|
||||
"""Returns canonicalized `text` (puncuation removed).
|
||||
|
||||
Args:
|
||||
text (`str`):
|
||||
String to be canonicalized.
|
||||
keep_punctuation_exact_string (`str`, *optional*):
|
||||
If provided, then this exact string is kept. For example providing '{}' will keep any occurrences of '{}'
|
||||
(but will still remove '{' and '}' that appear separately).
|
||||
"""
|
||||
if keep_punctuation_exact_string:
|
||||
text = keep_punctuation_exact_string.join(
|
||||
self.remove_punctuation(part) for part in text.split(keep_punctuation_exact_string)
|
||||
)
|
||||
else:
|
||||
text = self.remove_punctuation(text)
|
||||
text = re.sub(r"\s+", " ", text)
|
||||
text = text.strip()
|
||||
|
||||
return text
|
||||
|
||||
def tokenize(self, text: "TextInput", add_special_tokens=False, **kwargs) -> List[str]:
|
||||
"""
|
||||
Converts a string to a list of tokens.
|
||||
"""
|
||||
tokens = super().tokenize(SPIECE_UNDERLINE + text.replace(SPIECE_UNDERLINE, " "), **kwargs)
|
||||
|
||||
if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens:
|
||||
tokens = tokens[1:]
|
||||
return tokens
|
||||
|
||||
@property
|
||||
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.unk_token_length
|
||||
def unk_token_length(self):
|
||||
return len(self.sp_model.encode(str(self.unk_token)))
|
||||
|
||||
def _tokenize(self, text, **kwargs):
|
||||
"""
|
||||
Returns a tokenized string.
|
||||
|
||||
We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any
|
||||
SPIECE_UNDERLINE.
|
||||
|
||||
For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give `['H', 'e', 'y']` instead of `['▁He', 'y']`.
|
||||
|
||||
Thus we always encode `f"{unk_token}text"` and strip the `unk_token`. Here is an example with `unk_token = "<unk>"` and `unk_token_length = 4`.
|
||||
`self.tokenizer.sp_model.encode("<unk> Hey", out_type = str)[4:]`.
|
||||
"""
|
||||
text = self.canonicalize_text(text, keep_punctuation_exact_string=None)
|
||||
tokens = self.sp_model.encode(text, out_type=str)
|
||||
|
||||
# 1. Encode string + prefix ex: "<unk> Hey"
|
||||
tokens = self.sp_model.encode(self.unk_token + text, out_type=str)
|
||||
# 2. Remove self.unk_token from ['<','unk','>', '▁Hey']
|
||||
return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens
|
||||
|
||||
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._convert_token_to_id
|
||||
def _convert_token_to_id(self, token):
|
||||
"""Converts a token (str) in an id using the vocab."""
|
||||
return self.sp_model.piece_to_id(token)
|
||||
|
||||
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer._convert_id_to_token
|
||||
def _convert_id_to_token(self, index):
|
||||
"""Converts an index (integer) in a token (str) using the vocab."""
|
||||
token = self.sp_model.IdToPiece(index)
|
||||
return token
|
||||
|
||||
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.convert_tokens_to_string
|
||||
def convert_tokens_to_string(self, tokens):
|
||||
"""Converts a sequence of tokens (string) in a single string."""
|
||||
current_sub_tokens = []
|
||||
# since we manually add the prefix space, we have to remove it
|
||||
tokens[0] = tokens[0].lstrip(SPIECE_UNDERLINE)
|
||||
out_string = ""
|
||||
prev_is_special = False
|
||||
for token in tokens:
|
||||
# make sure that special tokens are not decoded using sentencepiece model
|
||||
if token in self.all_special_tokens:
|
||||
if not prev_is_special:
|
||||
out_string += " "
|
||||
out_string += self.sp_model.decode(current_sub_tokens) + token
|
||||
prev_is_special = True
|
||||
current_sub_tokens = []
|
||||
else:
|
||||
current_sub_tokens.append(token)
|
||||
prev_is_special = False
|
||||
out_string += self.sp_model.decode(current_sub_tokens)
|
||||
return out_string.strip()
|
||||
|
||||
# Copied from transformers.models.t5.tokenization_t5.T5Tokenizer.save_vocabulary
|
||||
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
||||
if not os.path.isdir(save_directory):
|
||||
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
|
||||
return
|
||||
out_vocab_file = os.path.join(
|
||||
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
|
||||
)
|
||||
|
||||
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
|
||||
copyfile(self.vocab_file, out_vocab_file)
|
||||
elif not os.path.isfile(self.vocab_file):
|
||||
with open(out_vocab_file, "wb") as fi:
|
||||
content_spiece_model = self.sp_model.serialized_model_proto()
|
||||
fi.write(content_spiece_model)
|
||||
|
||||
return (out_vocab_file,)
|
||||
|
|
@ -18,6 +18,8 @@ if is_vision_available():
|
|||
from ..image_utils import load_image
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES
|
||||
|
||||
if is_tf_available():
|
||||
|
|
@ -120,7 +122,8 @@ class ZeroShotImageClassificationPipeline(Pipeline):
|
|||
inputs = self.image_processor(images=[image], return_tensors=self.framework)
|
||||
inputs["candidate_labels"] = candidate_labels
|
||||
sequences = [hypothesis_template.format(x) for x in candidate_labels]
|
||||
text_inputs = self.tokenizer(sequences, return_tensors=self.framework, padding=True)
|
||||
padding = "max_length" if self.model.config.model_type == "siglip" else True
|
||||
text_inputs = self.tokenizer(sequences, return_tensors=self.framework, padding=padding)
|
||||
inputs["text_inputs"] = [text_inputs]
|
||||
return inputs
|
||||
|
||||
|
|
@ -144,7 +147,12 @@ class ZeroShotImageClassificationPipeline(Pipeline):
|
|||
def postprocess(self, model_outputs):
|
||||
candidate_labels = model_outputs.pop("candidate_labels")
|
||||
logits = model_outputs["logits"][0]
|
||||
if self.framework == "pt":
|
||||
if self.framework == "pt" and self.model.config.model_type == "siglip":
|
||||
probs = torch.sigmoid(logits).squeeze(-1)
|
||||
scores = probs.tolist()
|
||||
if not isinstance(scores, list):
|
||||
scores = [scores]
|
||||
elif self.framework == "pt":
|
||||
probs = logits.softmax(dim=-1).squeeze(-1)
|
||||
scores = probs.tolist()
|
||||
if not isinstance(scores, list):
|
||||
|
|
|
|||
|
|
@ -7514,6 +7514,37 @@ class SEWDPreTrainedModel(metaclass=DummyObject):
|
|||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
||||
|
||||
|
||||
class SiglipModel(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class SiglipPreTrainedModel(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class SiglipTextModel(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class SiglipVisionModel(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class SpeechEncoderDecoderModel(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
|
|
|
|||
|
|
@ -471,6 +471,13 @@ class SegformerImageProcessor(metaclass=DummyObject):
|
|||
requires_backends(self, ["vision"])
|
||||
|
||||
|
||||
class SiglipImageProcessor(metaclass=DummyObject):
|
||||
_backends = ["vision"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["vision"])
|
||||
|
||||
|
||||
class Swin2SRImageProcessor(metaclass=DummyObject):
|
||||
_backends = ["vision"]
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,125 @@
|
|||
# coding=utf-8
|
||||
# Copyright 2024 HuggingFace Inc.
|
||||
#
|
||||
# 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
|
||||
|
||||
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 SiglipImageProcessor
|
||||
|
||||
|
||||
class SiglipImageProcessingTester(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,
|
||||
do_rescale=True,
|
||||
rescale_factor=1 / 255,
|
||||
do_normalize=True,
|
||||
image_mean=[0.5, 0.5, 0.5],
|
||||
image_std=[0.5, 0.5, 0.5],
|
||||
):
|
||||
size = size if size is not None else {"height": 18, "width": 18}
|
||||
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
|
||||
self.do_rescale = do_rescale
|
||||
self.rescale_factor = rescale_factor
|
||||
self.do_normalize = do_normalize
|
||||
self.image_mean = image_mean
|
||||
self.image_std = image_std
|
||||
|
||||
def prepare_image_processor_dict(self):
|
||||
return {
|
||||
"do_resize": self.do_resize,
|
||||
"size": self.size,
|
||||
"do_rescale": self.do_rescale,
|
||||
"rescale_factor": self.rescale_factor,
|
||||
"do_normalize": self.do_normalize,
|
||||
"image_mean": self.image_mean,
|
||||
"image_std": self.image_std,
|
||||
}
|
||||
|
||||
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
|
||||
# Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest with CLIP->Siglip
|
||||
class SiglipImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
|
||||
image_processing_class = SiglipImageProcessor if is_vision_available() else None
|
||||
|
||||
def setUp(self):
|
||||
self.image_processor_tester = SiglipImageProcessingTester(self)
|
||||
|
||||
@property
|
||||
def image_processor_dict(self):
|
||||
return self.image_processor_tester.prepare_image_processor_dict()
|
||||
|
||||
# Ignore copy
|
||||
def test_image_processor_properties(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, "resample"))
|
||||
self.assertTrue(hasattr(image_processing, "do_rescale"))
|
||||
self.assertTrue(hasattr(image_processing, "rescale_factor"))
|
||||
self.assertTrue(hasattr(image_processing, "do_normalize"))
|
||||
self.assertTrue(hasattr(image_processing, "image_mean"))
|
||||
self.assertTrue(hasattr(image_processing, "image_std"))
|
||||
|
||||
# Ignore copy
|
||||
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": 18, "width": 18})
|
||||
|
||||
image_processor = self.image_processing_class.from_dict(
|
||||
self.image_processor_dict, size={"height": 84, "width": 84}
|
||||
)
|
||||
self.assertEqual(image_processor.size, {"height": 84, "width": 84})
|
||||
|
||||
@unittest.skip("not supported")
|
||||
# Ignore copy
|
||||
def test_call_numpy_4_channels(self):
|
||||
pass
|
||||
|
|
@ -0,0 +1,631 @@
|
|||
# coding=utf-8
|
||||
# Copyright 2024 The HuggingFace Inc. 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.
|
||||
""" Testing suite for the PyTorch Siglip model. """
|
||||
|
||||
|
||||
import inspect
|
||||
import os
|
||||
import tempfile
|
||||
import unittest
|
||||
|
||||
import numpy as np
|
||||
import requests
|
||||
|
||||
from transformers import SiglipConfig, SiglipTextConfig, SiglipVisionConfig
|
||||
from transformers.testing_utils import (
|
||||
require_torch,
|
||||
require_vision,
|
||||
slow,
|
||||
torch_device,
|
||||
)
|
||||
from transformers.utils import is_torch_available, is_vision_available
|
||||
|
||||
from ...test_configuration_common import ConfigTester
|
||||
from ...test_modeling_common import (
|
||||
ModelTesterMixin,
|
||||
_config_zero_init,
|
||||
floats_tensor,
|
||||
ids_tensor,
|
||||
random_attention_mask,
|
||||
)
|
||||
from ...test_pipeline_mixin import PipelineTesterMixin
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
from transformers import SiglipModel, SiglipTextModel, SiglipVisionModel
|
||||
from transformers.models.siglip.modeling_siglip import SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST
|
||||
|
||||
|
||||
if is_vision_available():
|
||||
from PIL import Image
|
||||
|
||||
from transformers import SiglipProcessor
|
||||
|
||||
|
||||
class SiglipVisionModelTester:
|
||||
def __init__(
|
||||
self,
|
||||
parent,
|
||||
batch_size=12,
|
||||
image_size=30,
|
||||
patch_size=2,
|
||||
num_channels=3,
|
||||
is_training=True,
|
||||
hidden_size=32,
|
||||
num_hidden_layers=2,
|
||||
num_attention_heads=4,
|
||||
intermediate_size=37,
|
||||
dropout=0.1,
|
||||
attention_dropout=0.1,
|
||||
initializer_range=0.02,
|
||||
scope=None,
|
||||
):
|
||||
self.parent = parent
|
||||
self.batch_size = batch_size
|
||||
self.image_size = image_size
|
||||
self.patch_size = patch_size
|
||||
self.num_channels = num_channels
|
||||
self.is_training = is_training
|
||||
self.hidden_size = hidden_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.intermediate_size = intermediate_size
|
||||
self.dropout = dropout
|
||||
self.attention_dropout = attention_dropout
|
||||
self.initializer_range = initializer_range
|
||||
self.scope = scope
|
||||
|
||||
# in ViT, the seq length equals the number of patches
|
||||
num_patches = (image_size // patch_size) ** 2
|
||||
self.seq_length = num_patches
|
||||
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPVisionModelTester.prepare_config_and_inputs
|
||||
def prepare_config_and_inputs(self):
|
||||
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
|
||||
config = self.get_config()
|
||||
|
||||
return config, pixel_values
|
||||
|
||||
def get_config(self):
|
||||
return SiglipVisionConfig(
|
||||
image_size=self.image_size,
|
||||
patch_size=self.patch_size,
|
||||
num_channels=self.num_channels,
|
||||
hidden_size=self.hidden_size,
|
||||
num_hidden_layers=self.num_hidden_layers,
|
||||
num_attention_heads=self.num_attention_heads,
|
||||
intermediate_size=self.intermediate_size,
|
||||
dropout=self.dropout,
|
||||
attention_dropout=self.attention_dropout,
|
||||
initializer_range=self.initializer_range,
|
||||
)
|
||||
|
||||
def create_and_check_model(self, config, pixel_values):
|
||||
model = SiglipVisionModel(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
with torch.no_grad():
|
||||
result = model(pixel_values)
|
||||
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
|
||||
image_size = (self.image_size, self.image_size)
|
||||
patch_size = (self.patch_size, self.patch_size)
|
||||
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
|
||||
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches, self.hidden_size))
|
||||
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
|
||||
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPVisionModelTester.prepare_config_and_inputs_for_common
|
||||
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 SiglipVisionModelTest(ModelTesterMixin, unittest.TestCase):
|
||||
"""
|
||||
Here we also overwrite some of the tests of test_modeling_common.py, as SIGLIP does not use input_ids, inputs_embeds,
|
||||
attention_mask and seq_length.
|
||||
"""
|
||||
|
||||
all_model_classes = (SiglipVisionModel,) if is_torch_available() else ()
|
||||
fx_compatible = False
|
||||
test_pruning = False
|
||||
test_resize_embeddings = False
|
||||
test_head_masking = False
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = SiglipVisionModelTester(self)
|
||||
self.config_tester = ConfigTester(
|
||||
self, config_class=SiglipVisionConfig, has_text_modality=False, hidden_size=37
|
||||
)
|
||||
|
||||
def test_config(self):
|
||||
self.config_tester.run_common_tests()
|
||||
|
||||
@unittest.skip(reason="SIGLIP does not use inputs_embeds")
|
||||
def test_inputs_embeds(self):
|
||||
pass
|
||||
|
||||
def test_model_common_attributes(self):
|
||||
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
model = model_class(config)
|
||||
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
|
||||
x = model.get_output_embeddings()
|
||||
self.assertTrue(x is None or isinstance(x, nn.Linear))
|
||||
|
||||
def test_forward_signature(self):
|
||||
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
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_model(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_model(*config_and_inputs)
|
||||
|
||||
@unittest.skip(reason="SiglipVisionModel does not support standalone training")
|
||||
def test_training(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="SiglipVisionModel does not support standalone training")
|
||||
def test_training_gradient_checkpointing(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="SiglipVisionModel does not support standalone training")
|
||||
def test_training_gradient_checkpointing_use_reentrant(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="SiglipVisionModel does not support standalone training")
|
||||
def test_training_gradient_checkpointing_use_reentrant_false(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="SiglipVisionModel has no base class and is not available in MODEL_MAPPING")
|
||||
def test_save_load_fast_init_from_base(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="SiglipVisionModel has no base class and is not available in MODEL_MAPPING")
|
||||
def test_save_load_fast_init_to_base(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Siglip uses the same initialization scheme as the Flax original implementation")
|
||||
def test_initialization(self):
|
||||
pass
|
||||
|
||||
@slow
|
||||
def test_model_from_pretrained(self):
|
||||
for model_name in SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
||||
model = SiglipVisionModel.from_pretrained(model_name)
|
||||
self.assertIsNotNone(model)
|
||||
|
||||
|
||||
class SiglipTextModelTester:
|
||||
def __init__(
|
||||
self,
|
||||
parent,
|
||||
batch_size=12,
|
||||
seq_length=7,
|
||||
is_training=True,
|
||||
use_input_mask=True,
|
||||
use_labels=True,
|
||||
vocab_size=99,
|
||||
hidden_size=32,
|
||||
num_hidden_layers=2,
|
||||
num_attention_heads=4,
|
||||
intermediate_size=37,
|
||||
dropout=0.1,
|
||||
attention_dropout=0.1,
|
||||
max_position_embeddings=512,
|
||||
initializer_range=0.02,
|
||||
scope=None,
|
||||
):
|
||||
self.parent = parent
|
||||
self.batch_size = batch_size
|
||||
self.seq_length = seq_length
|
||||
self.is_training = is_training
|
||||
self.use_input_mask = use_input_mask
|
||||
self.use_labels = use_labels
|
||||
self.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.intermediate_size = intermediate_size
|
||||
self.dropout = dropout
|
||||
self.attention_dropout = attention_dropout
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.initializer_range = initializer_range
|
||||
self.scope = scope
|
||||
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTester.prepare_config_and_inputs
|
||||
def prepare_config_and_inputs(self):
|
||||
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
|
||||
|
||||
input_mask = None
|
||||
if self.use_input_mask:
|
||||
input_mask = random_attention_mask([self.batch_size, self.seq_length])
|
||||
|
||||
if input_mask is not None:
|
||||
batch_size, seq_length = input_mask.shape
|
||||
rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
|
||||
for batch_idx, start_index in enumerate(rnd_start_indices):
|
||||
input_mask[batch_idx, :start_index] = 1
|
||||
input_mask[batch_idx, start_index:] = 0
|
||||
|
||||
config = self.get_config()
|
||||
|
||||
return config, input_ids, input_mask
|
||||
|
||||
def get_config(self):
|
||||
return SiglipTextConfig(
|
||||
vocab_size=self.vocab_size,
|
||||
hidden_size=self.hidden_size,
|
||||
num_hidden_layers=self.num_hidden_layers,
|
||||
num_attention_heads=self.num_attention_heads,
|
||||
intermediate_size=self.intermediate_size,
|
||||
dropout=self.dropout,
|
||||
attention_dropout=self.attention_dropout,
|
||||
max_position_embeddings=self.max_position_embeddings,
|
||||
initializer_range=self.initializer_range,
|
||||
)
|
||||
|
||||
def create_and_check_model(self, config, input_ids, input_mask):
|
||||
model = SiglipTextModel(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
with torch.no_grad():
|
||||
result = model(input_ids, attention_mask=input_mask)
|
||||
result = model(input_ids)
|
||||
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
|
||||
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
|
||||
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTester.prepare_config_and_inputs_for_common
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config_and_inputs = self.prepare_config_and_inputs()
|
||||
config, input_ids, input_mask = config_and_inputs
|
||||
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
|
||||
return config, inputs_dict
|
||||
|
||||
|
||||
@require_torch
|
||||
class SiglipTextModelTest(ModelTesterMixin, unittest.TestCase):
|
||||
all_model_classes = (SiglipTextModel,) if is_torch_available() else ()
|
||||
fx_compatible = False
|
||||
test_pruning = False
|
||||
test_head_masking = False
|
||||
model_split_percents = [0.5, 0.8, 0.9]
|
||||
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.setUp with CLIP->Siglip
|
||||
def setUp(self):
|
||||
self.model_tester = SiglipTextModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=SiglipTextConfig, hidden_size=37)
|
||||
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_config
|
||||
def test_config(self):
|
||||
self.config_tester.run_common_tests()
|
||||
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_model
|
||||
def test_model(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_model(*config_and_inputs)
|
||||
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_training
|
||||
def test_training(self):
|
||||
pass
|
||||
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_training_gradient_checkpointing
|
||||
def test_training_gradient_checkpointing(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(
|
||||
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
||||
)
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_training_gradient_checkpointing_use_reentrant
|
||||
def test_training_gradient_checkpointing_use_reentrant(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(
|
||||
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
||||
)
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_training_gradient_checkpointing_use_reentrant_false
|
||||
def test_training_gradient_checkpointing_use_reentrant_false(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Siglip does not use inputs_embeds")
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_inputs_embeds
|
||||
def test_inputs_embeds(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="SiglipTextModel has no base class and is not available in MODEL_MAPPING")
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_save_load_fast_init_from_base
|
||||
def test_save_load_fast_init_from_base(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="SiglipTextModel has no base class and is not available in MODEL_MAPPING")
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_save_load_fast_init_to_base
|
||||
def test_save_load_fast_init_to_base(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Siglip uses the same initialization scheme as the Flax original implementation")
|
||||
def test_initialization(self):
|
||||
pass
|
||||
|
||||
@slow
|
||||
def test_model_from_pretrained(self):
|
||||
for model_name in SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
||||
model = SiglipTextModel.from_pretrained(model_name)
|
||||
self.assertIsNotNone(model)
|
||||
|
||||
|
||||
class SiglipModelTester:
|
||||
def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):
|
||||
if text_kwargs is None:
|
||||
text_kwargs = {}
|
||||
if vision_kwargs is None:
|
||||
vision_kwargs = {}
|
||||
|
||||
self.parent = parent
|
||||
self.text_model_tester = SiglipTextModelTester(parent, **text_kwargs)
|
||||
self.vision_model_tester = SiglipVisionModelTester(parent, **vision_kwargs)
|
||||
self.is_training = is_training
|
||||
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPModelTester.prepare_config_and_inputs
|
||||
def prepare_config_and_inputs(self):
|
||||
text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
|
||||
vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
|
||||
|
||||
config = self.get_config()
|
||||
|
||||
return config, input_ids, attention_mask, pixel_values
|
||||
|
||||
def get_config(self):
|
||||
return SiglipConfig.from_text_vision_configs(
|
||||
self.text_model_tester.get_config(),
|
||||
self.vision_model_tester.get_config(),
|
||||
)
|
||||
|
||||
def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
|
||||
model = SiglipModel(config).to(torch_device).eval()
|
||||
with torch.no_grad():
|
||||
result = model(input_ids, pixel_values, attention_mask)
|
||||
self.parent.assertEqual(
|
||||
result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size)
|
||||
)
|
||||
self.parent.assertEqual(
|
||||
result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size)
|
||||
)
|
||||
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config_and_inputs = self.prepare_config_and_inputs()
|
||||
config, input_ids, attention_mask, pixel_values = config_and_inputs
|
||||
inputs_dict = {
|
||||
"input_ids": input_ids,
|
||||
"attention_mask": attention_mask,
|
||||
"pixel_values": pixel_values,
|
||||
"return_loss": False,
|
||||
}
|
||||
return config, inputs_dict
|
||||
|
||||
|
||||
@require_torch
|
||||
class SiglipModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
||||
all_model_classes = (SiglipModel,) if is_torch_available() else ()
|
||||
pipeline_model_mapping = {"feature-extraction": SiglipModel} if is_torch_available() else {}
|
||||
fx_compatible = False
|
||||
test_head_masking = False
|
||||
test_pruning = False
|
||||
test_resize_embeddings = False
|
||||
test_attention_outputs = False
|
||||
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.setUp with CLIP->Siglip
|
||||
def setUp(self):
|
||||
self.model_tester = SiglipModelTester(self)
|
||||
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_model
|
||||
def test_model(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_model(*config_and_inputs)
|
||||
|
||||
@unittest.skip(reason="Hidden_states is tested in individual model tests")
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_hidden_states_output
|
||||
def test_hidden_states_output(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Inputs_embeds is tested in individual model tests")
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_inputs_embeds
|
||||
def test_inputs_embeds(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Retain_grad is tested in individual model tests")
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_retain_grad_hidden_states_attentions
|
||||
def test_retain_grad_hidden_states_attentions(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="SiglipModel does not have input/output embeddings")
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_model_common_attributes
|
||||
def test_model_common_attributes(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="SiglipModel does not support training")
|
||||
def test_training(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="SiglipModel does not support training")
|
||||
def test_training_gradient_checkpointing(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="SiglipModel does not support training")
|
||||
def test_training_gradient_checkpointing_use_reentrant(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="SiglipModel does not support training")
|
||||
def test_training_gradient_checkpointing_use_reentrant_false(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Siglip uses the same initialization scheme as the Flax original implementation")
|
||||
def test_initialization(self):
|
||||
pass
|
||||
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPModelTest._create_and_check_torchscript with CLIP->Siglip
|
||||
def _create_and_check_torchscript(self, config, inputs_dict):
|
||||
if not self.test_torchscript:
|
||||
return
|
||||
|
||||
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
|
||||
configs_no_init.torchscript = True
|
||||
configs_no_init.return_dict = False
|
||||
for model_class in self.all_model_classes:
|
||||
model = model_class(config=configs_no_init)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
try:
|
||||
input_ids = inputs_dict["input_ids"]
|
||||
pixel_values = inputs_dict["pixel_values"] # Siglip needs pixel_values
|
||||
traced_model = torch.jit.trace(model, (input_ids, pixel_values))
|
||||
except RuntimeError:
|
||||
self.fail("Couldn't trace module.")
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
||||
pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
|
||||
|
||||
try:
|
||||
torch.jit.save(traced_model, pt_file_name)
|
||||
except Exception:
|
||||
self.fail("Couldn't save module.")
|
||||
|
||||
try:
|
||||
loaded_model = torch.jit.load(pt_file_name)
|
||||
except Exception:
|
||||
self.fail("Couldn't load module.")
|
||||
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
loaded_model.to(torch_device)
|
||||
loaded_model.eval()
|
||||
|
||||
model_state_dict = model.state_dict()
|
||||
loaded_model_state_dict = loaded_model.state_dict()
|
||||
|
||||
non_persistent_buffers = {}
|
||||
for key in loaded_model_state_dict.keys():
|
||||
if key not in model_state_dict.keys():
|
||||
non_persistent_buffers[key] = loaded_model_state_dict[key]
|
||||
|
||||
loaded_model_state_dict = {
|
||||
key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
|
||||
}
|
||||
|
||||
self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
|
||||
|
||||
model_buffers = list(model.buffers())
|
||||
for non_persistent_buffer in non_persistent_buffers.values():
|
||||
found_buffer = False
|
||||
for i, model_buffer in enumerate(model_buffers):
|
||||
if torch.equal(non_persistent_buffer, model_buffer):
|
||||
found_buffer = True
|
||||
break
|
||||
|
||||
self.assertTrue(found_buffer)
|
||||
model_buffers.pop(i)
|
||||
|
||||
models_equal = True
|
||||
for layer_name, p1 in model_state_dict.items():
|
||||
p2 = loaded_model_state_dict[layer_name]
|
||||
if p1.data.ne(p2.data).sum() > 0:
|
||||
models_equal = False
|
||||
|
||||
self.assertTrue(models_equal)
|
||||
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_load_vision_text_config with CLIP->Siglip
|
||||
def test_load_vision_text_config(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
# Save SiglipConfig and check if we can load SiglipVisionConfig from it
|
||||
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
||||
config.save_pretrained(tmp_dir_name)
|
||||
vision_config = SiglipVisionConfig.from_pretrained(tmp_dir_name)
|
||||
self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
|
||||
|
||||
# Save SiglipConfig and check if we can load SiglipTextConfig from it
|
||||
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
||||
config.save_pretrained(tmp_dir_name)
|
||||
text_config = SiglipTextConfig.from_pretrained(tmp_dir_name)
|
||||
self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())
|
||||
|
||||
@slow
|
||||
# Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_model_from_pretrained with CLIPModel->SiglipModel, CLIP->SIGLIP
|
||||
def test_model_from_pretrained(self):
|
||||
for model_name in SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
||||
model = SiglipModel.from_pretrained(model_name)
|
||||
self.assertIsNotNone(model)
|
||||
|
||||
|
||||
# We will verify our results on an image of cute cats
|
||||
def prepare_img():
|
||||
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
||||
image = Image.open(requests.get(url, stream=True).raw)
|
||||
return image
|
||||
|
||||
|
||||
@require_vision
|
||||
@require_torch
|
||||
class SiglipModelIntegrationTest(unittest.TestCase):
|
||||
@slow
|
||||
def test_inference(self):
|
||||
model_name = "google/siglip-base-patch16-224"
|
||||
model = SiglipModel.from_pretrained(model_name).to(torch_device)
|
||||
processor = SiglipProcessor.from_pretrained(model_name)
|
||||
|
||||
image = prepare_img()
|
||||
inputs = processor(
|
||||
text=["a photo of 2 cats", "a photo of 2 dogs"], images=image, padding="max_length", return_tensors="pt"
|
||||
).to(torch_device)
|
||||
|
||||
# forward pass
|
||||
with torch.no_grad():
|
||||
outputs = model(**inputs)
|
||||
logits_per_image = outputs.logits_per_image
|
||||
logits_per_text = outputs.logits_per_text
|
||||
|
||||
# verify the logits
|
||||
self.assertEqual(
|
||||
logits_per_image.shape,
|
||||
torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
|
||||
)
|
||||
self.assertEqual(
|
||||
logits_per_text.shape,
|
||||
torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
|
||||
)
|
||||
|
||||
expected_logits = torch.tensor([[-0.7567, -10.3354]], device=torch_device)
|
||||
|
||||
self.assertTrue(torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3))
|
||||
|
||||
# verify the probs
|
||||
probs = torch.sigmoid(logits_per_image) # these are the probabilities
|
||||
expected_probs = torch.tensor([[3.1937e-01, 3.2463e-05]], device=torch_device)
|
||||
self.assertTrue(torch.allclose(probs, expected_probs, atol=1e-3))
|
||||
|
|
@ -0,0 +1,462 @@
|
|||
# coding=utf-8
|
||||
# Copyright 2024 The HuggingFace Inc. team.
|
||||
#
|
||||
# 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 json
|
||||
import os
|
||||
import tempfile
|
||||
import unittest
|
||||
|
||||
from transformers import SPIECE_UNDERLINE, AddedToken, BatchEncoding, SiglipTokenizer
|
||||
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
|
||||
from transformers.utils import cached_property, is_tf_available, is_torch_available
|
||||
|
||||
from ...test_tokenization_common import TokenizerTesterMixin
|
||||
|
||||
|
||||
SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
|
||||
|
||||
if is_torch_available():
|
||||
FRAMEWORK = "pt"
|
||||
elif is_tf_available():
|
||||
FRAMEWORK = "tf"
|
||||
else:
|
||||
FRAMEWORK = "jax"
|
||||
|
||||
|
||||
@require_sentencepiece
|
||||
@require_tokenizers
|
||||
class SiglipTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
|
||||
tokenizer_class = SiglipTokenizer
|
||||
test_rust_tokenizer = False
|
||||
test_sentencepiece = True
|
||||
test_sentencepiece_ignore_case = True
|
||||
|
||||
# Copied from tests.models.t5.test_tokenization_t5.T5TokenizationTest.setUp with T5->Siglip
|
||||
def setUp(self):
|
||||
super().setUp()
|
||||
|
||||
# We have a SentencePiece fixture for testing
|
||||
tokenizer = SiglipTokenizer(SAMPLE_VOCAB)
|
||||
tokenizer.save_pretrained(self.tmpdirname)
|
||||
|
||||
# Copied from tests.models.t5.test_tokenization_t5.T5TokenizationTest.test_convert_token_and_id with T5->Siglip
|
||||
def test_convert_token_and_id(self):
|
||||
"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
|
||||
token = "<s>"
|
||||
token_id = 1
|
||||
|
||||
self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
|
||||
self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
|
||||
|
||||
def test_get_vocab(self):
|
||||
vocab_keys = list(self.get_tokenizer().get_vocab().keys())
|
||||
|
||||
self.assertEqual(vocab_keys[0], "<unk>")
|
||||
self.assertEqual(vocab_keys[1], "<s>")
|
||||
|
||||
def test_full_tokenizer(self):
|
||||
tokenizer = SiglipTokenizer(SAMPLE_VOCAB)
|
||||
|
||||
tokens = tokenizer.tokenize("This is a test")
|
||||
self.assertListEqual(tokens, ["▁this", "▁is", "▁a", "▁t", "est"])
|
||||
|
||||
self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [66, 46, 10, 170, 382])
|
||||
|
||||
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
|
||||
self.assertListEqual(
|
||||
tokens,
|
||||
[
|
||||
SPIECE_UNDERLINE,
|
||||
"i",
|
||||
SPIECE_UNDERLINE + "was",
|
||||
SPIECE_UNDERLINE + "b",
|
||||
"or",
|
||||
"n",
|
||||
SPIECE_UNDERLINE + "in",
|
||||
SPIECE_UNDERLINE + "",
|
||||
"9",
|
||||
"2",
|
||||
"0",
|
||||
"0",
|
||||
"0",
|
||||
SPIECE_UNDERLINE + "and",
|
||||
SPIECE_UNDERLINE + "this",
|
||||
SPIECE_UNDERLINE + "is",
|
||||
SPIECE_UNDERLINE + "f",
|
||||
"al",
|
||||
"s",
|
||||
"é",
|
||||
],
|
||||
)
|
||||
ids = tokenizer.convert_tokens_to_ids(tokens)
|
||||
self.assertListEqual(ids, [7, 23, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 12, 66, 46, 72, 80, 6, 0])
|
||||
|
||||
back_tokens = tokenizer.convert_ids_to_tokens(ids)
|
||||
self.assertListEqual(
|
||||
back_tokens,
|
||||
[
|
||||
SPIECE_UNDERLINE,
|
||||
"i",
|
||||
SPIECE_UNDERLINE + "was",
|
||||
SPIECE_UNDERLINE + "b",
|
||||
"or",
|
||||
"n",
|
||||
SPIECE_UNDERLINE + "in",
|
||||
SPIECE_UNDERLINE + "",
|
||||
"<unk>",
|
||||
"2",
|
||||
"0",
|
||||
"0",
|
||||
"0",
|
||||
SPIECE_UNDERLINE + "and",
|
||||
SPIECE_UNDERLINE + "this",
|
||||
SPIECE_UNDERLINE + "is",
|
||||
SPIECE_UNDERLINE + "f",
|
||||
"al",
|
||||
"s",
|
||||
"<unk>",
|
||||
],
|
||||
)
|
||||
|
||||
@cached_property
|
||||
def siglip_tokenizer(self):
|
||||
return SiglipTokenizer.from_pretrained("google/siglip-base-patch16-224")
|
||||
|
||||
# Copied from tests.models.t5.test_tokenization_t5.T5TokenizationTest.get_tokenizer with T5->Siglip
|
||||
def get_tokenizer(self, **kwargs) -> SiglipTokenizer:
|
||||
return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
|
||||
|
||||
# Copied from tests.models.t5.test_tokenization_t5.T5TokenizationTest.test_rust_and_python_full_tokenizers with T5->Siglip
|
||||
def test_rust_and_python_full_tokenizers(self):
|
||||
if not self.test_rust_tokenizer:
|
||||
return
|
||||
|
||||
tokenizer = self.get_tokenizer()
|
||||
rust_tokenizer = self.get_rust_tokenizer()
|
||||
|
||||
sequence = "I was born in 92000, and this is falsé."
|
||||
|
||||
tokens = tokenizer.tokenize(sequence)
|
||||
rust_tokens = rust_tokenizer.tokenize(sequence)
|
||||
self.assertListEqual(tokens, rust_tokens)
|
||||
|
||||
ids = tokenizer.encode(sequence, add_special_tokens=False)
|
||||
rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
|
||||
self.assertListEqual(ids, rust_ids)
|
||||
|
||||
rust_tokenizer = self.get_rust_tokenizer()
|
||||
ids = tokenizer.encode(sequence)
|
||||
rust_ids = rust_tokenizer.encode(sequence)
|
||||
self.assertListEqual(ids, rust_ids)
|
||||
|
||||
def test_eos_treatment(self):
|
||||
tokenizer = self.siglip_tokenizer
|
||||
batch_with_eos_added = tokenizer(["hi</s>", "I went to the gym</s>", "</s>"])
|
||||
batch_without_eos_added = tokenizer(["hi", "I went to the gym", ""])
|
||||
self.assertListEqual(batch_with_eos_added["input_ids"], batch_without_eos_added["input_ids"])
|
||||
|
||||
def test_prepare_batch(self):
|
||||
tokenizer = self.siglip_tokenizer
|
||||
src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
|
||||
expected_src_tokens = [262, 266, 476, 8532, 270, 4460, 3949, 1682, tokenizer.eos_token_id]
|
||||
batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK)
|
||||
self.assertIsInstance(batch, BatchEncoding)
|
||||
|
||||
if FRAMEWORK != "jax":
|
||||
result = list(batch.input_ids.numpy()[0])
|
||||
else:
|
||||
result = list(batch.input_ids.tolist()[0])
|
||||
|
||||
self.assertListEqual(expected_src_tokens, result)
|
||||
|
||||
self.assertEqual((2, 9), batch.input_ids.shape)
|
||||
|
||||
def test_empty_target_text(self):
|
||||
tokenizer = self.siglip_tokenizer
|
||||
src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
|
||||
batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK)
|
||||
# check if input_ids are returned and no decoder_input_ids
|
||||
self.assertIn("input_ids", batch)
|
||||
self.assertNotIn("decoder_input_ids", batch)
|
||||
self.assertNotIn("decoder_attention_mask", batch)
|
||||
|
||||
def test_max_length(self):
|
||||
tokenizer = self.siglip_tokenizer
|
||||
tgt_text = ["Summary of the text.", "Another summary."]
|
||||
targets = tokenizer(
|
||||
text_target=tgt_text, max_length=32, padding="max_length", truncation=True, return_tensors=FRAMEWORK
|
||||
)
|
||||
self.assertEqual(32, targets["input_ids"].shape[1])
|
||||
|
||||
def test_eos_in_input(self):
|
||||
tokenizer = self.siglip_tokenizer
|
||||
src_text = ["A long paragraph for summarization. </s>"]
|
||||
tgt_text = ["Summary of the text. </s>"]
|
||||
expected_src_tokens = [262, 266, 476, 8532, 270, 4460, 3949, 1682, 1]
|
||||
expected_tgt_tokens = [6254, 267, 260, 1443, 1]
|
||||
|
||||
batch = tokenizer(src_text, text_target=tgt_text)
|
||||
|
||||
self.assertEqual(expected_src_tokens, batch["input_ids"][0])
|
||||
self.assertEqual(expected_tgt_tokens, batch["labels"][0])
|
||||
|
||||
@unittest.skip(reason="SiglipTokenizer strips the punctuation")
|
||||
def test_subword_regularization_tokenizer(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="SiglipTokenizer strips the punctuation")
|
||||
def test_pickle_subword_regularization_tokenizer(self):
|
||||
pass
|
||||
|
||||
# Copied from tests.models.t5.test_tokenization_t5.T5TokenizationTest.test_special_tokens_initialization with T5->Siglip
|
||||
def test_special_tokens_initialization(self):
|
||||
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
|
||||
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
|
||||
added_tokens = [f"<extra_id_{i}>" for i in range(100)] + [AddedToken("<special>", lstrip=True)]
|
||||
|
||||
tokenizer_r = self.rust_tokenizer_class.from_pretrained(
|
||||
pretrained_name, additional_special_tokens=added_tokens, **kwargs
|
||||
)
|
||||
tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
|
||||
pretrained_name, additional_special_tokens=added_tokens, **kwargs, from_slow=True
|
||||
)
|
||||
tokenizer_p = self.tokenizer_class.from_pretrained(
|
||||
pretrained_name, additional_special_tokens=added_tokens, **kwargs
|
||||
)
|
||||
|
||||
p_output = tokenizer_p.encode("Hey this is a <special> token")
|
||||
r_output = tokenizer_r.encode("Hey this is a <special> token")
|
||||
cr_output = tokenizer_cr.encode("Hey this is a <special> token")
|
||||
|
||||
special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0]
|
||||
|
||||
self.assertEqual(p_output, r_output)
|
||||
self.assertEqual(cr_output, r_output)
|
||||
self.assertTrue(special_token_id in p_output)
|
||||
self.assertTrue(special_token_id in r_output)
|
||||
self.assertTrue(special_token_id in cr_output)
|
||||
|
||||
# Copied from tests.models.t5.test_tokenization_t5.T5TokenizationTest.test_special_tokens_initialization_with_non_empty_additional_special_tokens with T5->Siglip
|
||||
def test_special_tokens_initialization_with_non_empty_additional_special_tokens(self):
|
||||
tokenizer_list = []
|
||||
if self.test_slow_tokenizer:
|
||||
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))
|
||||
|
||||
if self.test_rust_tokenizer:
|
||||
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))
|
||||
|
||||
for tokenizer_class, tokenizer_utils in tokenizer_list:
|
||||
with tempfile.TemporaryDirectory() as tmp_dir:
|
||||
tokenizer_utils.save_pretrained(tmp_dir)
|
||||
|
||||
with open(os.path.join(tmp_dir, "special_tokens_map.json"), encoding="utf-8") as json_file:
|
||||
special_tokens_map = json.load(json_file)
|
||||
|
||||
with open(os.path.join(tmp_dir, "tokenizer_config.json"), encoding="utf-8") as json_file:
|
||||
tokenizer_config = json.load(json_file)
|
||||
|
||||
added_tokens_extra_ids = [f"<extra_id_{i}>" for i in range(100)]
|
||||
|
||||
special_tokens_map["additional_special_tokens"] = added_tokens_extra_ids + [
|
||||
"an_additional_special_token"
|
||||
]
|
||||
tokenizer_config["additional_special_tokens"] = added_tokens_extra_ids + [
|
||||
"an_additional_special_token"
|
||||
]
|
||||
|
||||
with open(os.path.join(tmp_dir, "special_tokens_map.json"), "w", encoding="utf-8") as outfile:
|
||||
json.dump(special_tokens_map, outfile)
|
||||
with open(os.path.join(tmp_dir, "tokenizer_config.json"), "w", encoding="utf-8") as outfile:
|
||||
json.dump(tokenizer_config, outfile)
|
||||
|
||||
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
|
||||
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
|
||||
# "special_tokens_map.json" files
|
||||
tokenizer_without_change_in_init = tokenizer_class.from_pretrained(
|
||||
tmp_dir,
|
||||
)
|
||||
self.assertIn(
|
||||
"an_additional_special_token", tokenizer_without_change_in_init.additional_special_tokens
|
||||
)
|
||||
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # BySiglipTokenization no vocab
|
||||
self.assertEqual(
|
||||
["an_additional_special_token"],
|
||||
tokenizer_without_change_in_init.convert_ids_to_tokens(
|
||||
tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"])
|
||||
),
|
||||
)
|
||||
|
||||
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
|
||||
new_added_tokens = added_tokens_extra_ids + [AddedToken("a_new_additional_special_token", lstrip=True)]
|
||||
tokenizer = tokenizer_class.from_pretrained(
|
||||
tmp_dir,
|
||||
additional_special_tokens=new_added_tokens,
|
||||
)
|
||||
|
||||
self.assertIn("a_new_additional_special_token", tokenizer.additional_special_tokens)
|
||||
self.assertEqual(
|
||||
["a_new_additional_special_token"],
|
||||
tokenizer.convert_ids_to_tokens(
|
||||
tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"])
|
||||
),
|
||||
)
|
||||
|
||||
def test_sentencepiece_tokenize_and_convert_tokens_to_string(self):
|
||||
"""Test ``_tokenize`` and ``convert_tokens_to_string``."""
|
||||
if not self.test_sentencepiece:
|
||||
return
|
||||
|
||||
tokenizer = self.get_tokenizer()
|
||||
text = "This is text to test the tokenizer."
|
||||
|
||||
if self.test_sentencepiece_ignore_case:
|
||||
text = text.lower()
|
||||
|
||||
tokens = tokenizer.tokenize(text)
|
||||
|
||||
self.assertTrue(len(tokens) > 0)
|
||||
|
||||
# check if converting back to original text works
|
||||
reverse_text = tokenizer.convert_tokens_to_string(tokens)
|
||||
|
||||
if self.test_sentencepiece_ignore_case:
|
||||
reverse_text = reverse_text.lower()
|
||||
|
||||
expected_text = "this is text to test the tokenizer"
|
||||
self.assertEqual(reverse_text, expected_text)
|
||||
|
||||
special_tokens = tokenizer.all_special_tokens
|
||||
special_tokens_string = tokenizer.convert_tokens_to_string(special_tokens)
|
||||
for special_token in special_tokens:
|
||||
self.assertIn(special_token, special_tokens_string)
|
||||
|
||||
if self.test_rust_tokenizer:
|
||||
rust_tokenizer = self.get_rust_tokenizer()
|
||||
special_tokens_string_rust = rust_tokenizer.convert_tokens_to_string(special_tokens)
|
||||
self.assertEqual(special_tokens_string, special_tokens_string_rust)
|
||||
|
||||
# overwritten from `test_tokenization_common` since Siglip has no max length
|
||||
# Copied from tests.models.t5.test_tokenization_t5.T5TokenizationTest.test_pretrained_model_lists with T5->Siglip
|
||||
def test_pretrained_model_lists(self):
|
||||
# We should have at least one default checkpoint for each tokenizer
|
||||
# We should specify the max input length as well (used in some part to list the pretrained checkpoints)
|
||||
self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map), 1)
|
||||
self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0]), 1)
|
||||
|
||||
@slow
|
||||
def test_tokenizer_integration(self):
|
||||
tokenizer = SiglipTokenizer.from_pretrained("google/siglip-base-patch16-224")
|
||||
|
||||
# fmt: off
|
||||
texts = [
|
||||
'the real mountain view',
|
||||
'Zürich',
|
||||
'San Francisco',
|
||||
'a picture of a laptop with the lockscreen on, a cup of cappucino, salt and pepper grinders. The view through the window reveals lake Zürich and the Alps in the background of the city.',
|
||||
]
|
||||
|
||||
expected_input_ids = [
|
||||
[260, 638, 3293, 870, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
|
||||
[262, 761, 5879, 5345, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
|
||||
[262, 264, 452, 20563, 15949, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
|
||||
[262, 266, 1357, 267, 262, 266, 4429, 275, 260, 3940, 6360, 277, 262, 266, 3064, 267, 3549, 388, 16538, 296, 298, 2617, 263, 4869, 14998, 264, 260, 870, 393, 260, 1710, 7958, 4324, 262, 761, 5879, 5345, 263, 260, 1518, 388, 264, 268, 260, 1970, 267, 260, 741, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
|
||||
]
|
||||
# fmt: on
|
||||
|
||||
for text, expected in zip(texts, expected_input_ids):
|
||||
input_ids = tokenizer(text, padding="max_length").input_ids
|
||||
self.assertListEqual(input_ids, expected)
|
||||
|
||||
def test_some_edge_cases(self):
|
||||
tokenizer = SiglipTokenizer.from_pretrained("google/siglip-base-patch16-224", legacy=False)
|
||||
|
||||
sp_tokens = tokenizer.sp_model.encode("</s>>", out_type=str)
|
||||
self.assertEqual(sp_tokens, ["</", "s", ">", ">"])
|
||||
tokens = tokenizer.tokenize("</s>>")
|
||||
self.assertNotEqual(sp_tokens, tokens)
|
||||
self.assertEqual(tokens, ["</s>"])
|
||||
|
||||
tokens = tokenizer.tokenize("")
|
||||
self.assertEqual(tokens, [])
|
||||
self.assertEqual(tokens, tokenizer.sp_model.encode("", out_type=str))
|
||||
|
||||
tokens = tokenizer.tokenize(" ")
|
||||
self.assertEqual(tokens, [])
|
||||
self.assertEqual(tokens, tokenizer.sp_model.encode(" ", out_type=str))
|
||||
|
||||
tokens = tokenizer.tokenize("▁")
|
||||
self.assertEqual(tokens, [])
|
||||
self.assertEqual(tokens, tokenizer.sp_model.encode("▁", out_type=str))
|
||||
|
||||
tokens = tokenizer.tokenize(" ▁")
|
||||
self.assertEqual(tokens, [])
|
||||
self.assertEqual(tokens, tokenizer.sp_model.encode("▁", out_type=str))
|
||||
|
||||
|
||||
@require_sentencepiece
|
||||
@require_tokenizers
|
||||
class CommonSpmIntegrationTests(unittest.TestCase):
|
||||
"""
|
||||
A class that regroups important test to make sure that we properly handle the special tokens.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
tokenizer = SiglipTokenizer(SAMPLE_VOCAB, extra_ids=0, legacy=False)
|
||||
tokenizer.add_special_tokens(
|
||||
{"additional_special_tokens": [AddedToken("<extra_id_0>", rstrip=False, lstrip=False)]}
|
||||
)
|
||||
cls.tokenizer = tokenizer
|
||||
|
||||
def test_add_dummy_prefix(self):
|
||||
# make sure `'▁'` is prepended, and outputs match sp_model's
|
||||
# `sentencepiece.NormalizerSpec.add_dummy_prefix` attribute
|
||||
input_ids = self.tokenizer.encode(". Hello", add_special_tokens=False)
|
||||
self.assertEqual(input_ids, [37, 86, 20])
|
||||
self.assertEqual(input_ids, [37, 86, 20])
|
||||
tokens = self.tokenizer.tokenize(". Hello")
|
||||
self.assertEqual(tokens, ["▁he", "ll", "o"])
|
||||
|
||||
tokens = self.tokenizer.tokenize("")
|
||||
self.assertEqual(tokens, [])
|
||||
self.assertEqual(tokens, self.tokenizer.sp_model.encode("", out_type=str))
|
||||
|
||||
tokens = self.tokenizer.tokenize(" ")
|
||||
self.assertEqual(tokens, [])
|
||||
self.assertEqual(tokens, self.tokenizer.sp_model.encode(" ", out_type=str))
|
||||
|
||||
tokens = self.tokenizer.tokenize("▁")
|
||||
self.assertEqual(tokens, [])
|
||||
self.assertEqual(tokens, self.tokenizer.sp_model.encode("▁", out_type=str))
|
||||
|
||||
def test_remove_extra_whitespaces(self):
|
||||
# make sure the extra spaces are eaten
|
||||
# sentencepiece.NormalizerSpec.remove_extra_whitespaces attribute
|
||||
input_ids = self.tokenizer.encode(" . Hello", add_special_tokens=False)
|
||||
self.assertEqual(input_ids, [37, 86, 20])
|
||||
self.assertEqual(input_ids, [37, 86, 20])
|
||||
tokens = self.tokenizer.tokenize(" . Hello")
|
||||
self.assertEqual(tokens, ["▁he", "ll", "o"])
|
||||
|
||||
# `'▁'` is also a whitespace
|
||||
input_ids = self.tokenizer.encode("▁He is not")
|
||||
self.assertEqual(input_ids, [37, 46, 44, 2])
|
||||
tokens = self.tokenizer.tokenize("▁He is not")
|
||||
self.assertEqual(tokens, ["▁he", "▁is", "▁not"]) # no extra space added
|
||||
|
||||
input_ids = self.tokenizer.encode("▁He is not ▁He")
|
||||
self.assertEqual(input_ids, [37, 46, 44, 37, 2])
|
||||
tokens = self.tokenizer.tokenize("▁He is not ▁He")
|
||||
self.assertEqual(tokens, ["▁he", "▁is", "▁not", "▁he"]) # spaces are eaten by spm even if not start
|
||||
|
|
@ -241,3 +241,37 @@ class ZeroShotImageClassificationPipelineTests(unittest.TestCase):
|
|||
]
|
||||
* 5,
|
||||
)
|
||||
|
||||
@slow
|
||||
@require_torch
|
||||
def test_siglip_model_pt(self):
|
||||
image_classifier = pipeline(
|
||||
task="zero-shot-image-classification",
|
||||
model="google/siglip-base-patch16-224",
|
||||
)
|
||||
# This is an image of 2 cats with remotes and no planes
|
||||
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
|
||||
output = image_classifier(image, candidate_labels=["2 cats", "a plane", "a remote"])
|
||||
|
||||
self.assertEqual(
|
||||
nested_simplify(output),
|
||||
[
|
||||
{"score": 0.198, "label": "2 cats"},
|
||||
{"score": 0.0, "label": "a remote"},
|
||||
{"score": 0.0, "label": "a plane"},
|
||||
],
|
||||
)
|
||||
|
||||
output = image_classifier([image] * 5, candidate_labels=["2 cats", "a plane", "a remote"], batch_size=2)
|
||||
|
||||
self.assertEqual(
|
||||
nested_simplify(output),
|
||||
[
|
||||
[
|
||||
{"score": 0.198, "label": "2 cats"},
|
||||
{"score": 0.0, "label": "a remote"},
|
||||
{"score": 0.0, "label": "a plane"},
|
||||
]
|
||||
]
|
||||
* 5,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1061,6 +1061,7 @@ MODELS_NOT_IN_README = [
|
|||
"Vision Encoder decoder",
|
||||
"VisionTextDualEncoder",
|
||||
"CLIPVisionModel",
|
||||
"SiglipVisionModel",
|
||||
]
|
||||
|
||||
# Template for new entries to add in the main README when we have missing models.
|
||||
|
|
|
|||
|
|
@ -214,7 +214,6 @@ IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
|
|||
"ChineseCLIPVisionModel",
|
||||
"CLIPTextModel",
|
||||
"CLIPTextModelWithProjection",
|
||||
"CLIPVisionModel",
|
||||
"CLIPVisionModelWithProjection",
|
||||
"ClvpForCausalLM",
|
||||
"ClvpModel",
|
||||
|
|
@ -309,6 +308,8 @@ IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
|
|||
"SeamlessM4Tv2NARTextToUnitForConditionalGeneration",
|
||||
"SeamlessM4Tv2CodeHifiGan",
|
||||
"SeamlessM4Tv2ForSpeechToSpeech", # no auto class for speech-to-speech
|
||||
"SiglipVisionModel",
|
||||
"SiglipTextModel",
|
||||
]
|
||||
|
||||
# DO NOT edit this list!
|
||||
|
|
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@ -171,7 +171,7 @@ MODEL_NAMES_WITH_SAME_CONFIG = {
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"XLS-R": "Wav2Vec2",
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||||
"XLSR-Wav2Vec2": "Wav2Vec2",
|
||||
}
|
||||
MODEL_NAMES_TO_IGNORE = ["CLIPVisionModel"]
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||||
MODEL_NAMES_TO_IGNORE = ["CLIPVisionModel", "SiglipVisionModel"]
|
||||
|
||||
|
||||
def get_model_table_from_auto_modules() -> str:
|
||||
|
|
|
|||
Loading…
Reference in New Issue