Add OWLv2, bis (#26668)
* First draft * Update conversion script * Update copied from statements * Fix style * Add copied from to config * Add copied from to processor * Run make fixup * Add docstring * Update docstrings * Add method * Improve docstrings * Fix docstrings * Improve docstrings * Remove onnx * Add flag * Address comments * Add copied from to model tests * Add flag to conversion script * Add code snippet * Address more comments * Address comment * Improve conversion script * More improvements * Add expected objectness logits * Skip test * Improve conversion script * Extend conversion script * Convert large checkpoint * Fix doc tests * Convert all checkpoints, update integration tests * Add checkpoint_path arg * Fix repo_id
This commit is contained in:
NielsRogge
@@ -433,6 +433,7 @@ Current number of checkpoints: ** (from [s-JoL](https://huggingface.co/s-JoL)) released in [Open-Llama](https://github.com/s-JoL/Open-Llama).
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1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
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1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (from Google AI) released with the paper [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby.
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1. **[OWLv2](https://huggingface.co/docs/transformers/main/model_doc/owlv2)** (from Google AI) released with the paper [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) by Matthias Minderer, Alexey Gritsenko, Neil Houlsby.
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1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
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1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (from Google) released with the paper [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347) by Jason Phang, Yao Zhao, and Peter J. Liu.
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1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
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@@ -409,6 +409,7 @@ Número actual de puntos de control: ** (from [s-JoL](https://huggingface.co/s-JoL)) released in [Open-Llama](https://github.com/s-JoL/Open-Llama).
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1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
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1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (from Google AI) released with the paper [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby.
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1. **[OWLv2](https://huggingface.co/docs/transformers/main/model_doc/owlv2)** (from Google AI) released with the paper [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) by Matthias Minderer, Alexey Gritsenko, Neil Houlsby.
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1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
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1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (from Google) released with the paper [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347) by Jason Phang, Yao Zhao, and Peter J. Liu.
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1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
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@@ -381,6 +381,7 @@ conda install -c huggingface transformers
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1. **[OpenLlama](https://huggingface.co/docs/transformers/model_doc/open-llama)** (from [s-JoL](https://huggingface.co/s-JoL)) released in [Open-Llama](https://github.com/s-JoL/Open-Llama).
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1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
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1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (Google AI से) साथ में कागज [विज़न ट्रांसफॉर्मर्स के साथ सिंपल ओपन-वोकैबुलरी ऑब्जेक्ट डिटेक्शन](https:/ /arxiv.org/abs/2205.06230) मैथियास मिंडरर, एलेक्सी ग्रिट्सेंको, ऑस्टिन स्टोन, मैक्सिम न्यूमैन, डिर्क वीसेनबोर्न, एलेक्सी डोसोवित्स्की, अरविंद महेंद्रन, अनुराग अर्नब, मुस्तफा देहघानी, ज़ुओरन शेन, जिओ वांग, ज़ियाओहुआ झाई, थॉमस किफ़, और नील हॉल्सबी द्वारा पोस्ट किया गया।
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1. **[OWLv2](https://huggingface.co/docs/transformers/main/model_doc/owlv2)** (Google AI से) Matthias Minderer, Alexey Gritsenko, Neil Houlsby. द्वाराअनुसंधान पत्र [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) के साथ जारी किया गया
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1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
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1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (Google की ओर से) साथ में दिया गया पेपर [लंबे इनपुट सारांश के लिए ट्रांसफ़ॉर्मरों को बेहतर तरीके से एक्सटेंड करना](https://arxiv .org/abs/2208.04347) जेसन फांग, याओ झाओ, पीटर जे लियू द्वारा।
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1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (दीपमाइंड से) साथ में पेपर [पर्सीवर आईओ: संरचित इनपुट और आउटपुट के लिए एक सामान्य वास्तुकला] (https://arxiv.org/abs/2107.14795) एंड्रयू जेगल, सेबेस्टियन बोरग्यूड, जीन-बैप्टिस्ट अलायराक, कार्ल डोर्श, कैटलिन इओनेस्कु, डेविड द्वारा डिंग, स्कंद कोप्पुला, डैनियल ज़ोरान, एंड्रयू ब्रॉक, इवान शेलहैमर, ओलिवियर हेनाफ, मैथ्यू एम। बोट्विनिक, एंड्रयू ज़िसरमैन, ओरिओल विनियल्स, जोआओ कैरेरा द्वारा पोस्ट किया गया।
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@@ -443,6 +443,7 @@ Flax、PyTorch、TensorFlowをcondaでインストールする方法は、それ
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1. **[OpenLlama](https://huggingface.co/docs/transformers/model_doc/open-llama)** (from [s-JoL](https://huggingface.co/s-JoL)) released in [Open-Llama](https://github.com/s-JoL/Open-Llama).
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1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (Meta AI から) Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al から公開された研究論文: [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068)
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1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (Google AI から) Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby から公開された研究論文: [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230)
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1. **[OWLv2](https://huggingface.co/docs/transformers/main/model_doc/owlv2)** (Google AI から) Matthias Minderer, Alexey Gritsenko, Neil Houlsby. から公開された研究論文 [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683)
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1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (Google から) Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu から公開された研究論文: [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777)
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1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (Google から) Jason Phang, Yao Zhao, and Peter J. Liu から公開された研究論文: [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347)
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1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (Deepmind から) Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira から公開された研究論文: [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795)
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@@ -358,6 +358,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
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1. **[OpenLlama](https://huggingface.co/docs/transformers/model_doc/open-llama)** (from [s-JoL](https://huggingface.co/s-JoL)) released in [Open-Llama](https://github.com/s-JoL/Open-Llama).
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1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (Meta AI 에서) Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al 의 [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) 논문과 함께 발표했습니다.
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1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (Google AI 에서) Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby 의 [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) 논문과 함께 발표했습니다.
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1. **[OWLv2](https://huggingface.co/docs/transformers/main/model_doc/owlv2)** (Google AI 에서 제공)은 Matthias Minderer, Alexey Gritsenko, Neil Houlsby.의 [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683)논문과 함께 발표했습니다.
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1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (Google 에서) Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu 의 [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) 논문과 함께 발표했습니다.
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1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (Google 에서) Jason Phang, Yao Zhao, Peter J. Liu 의 [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347) 논문과 함께 발표했습니다.
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1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (Deepmind 에서) Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira 의 [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) 논문과 함께 발표했습니다.
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@@ -382,6 +382,7 @@ conda install -c huggingface transformers
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1. **[OpenLlama](https://huggingface.co/docs/transformers/model_doc/open-llama)** (来自 [s-JoL](https://huggingface.co/s-JoL)) 由 [Open-Llama](https://github.com/s-JoL/Open-Llama) 发布.
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1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (来自 Meta AI) 伴随论文 [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) 由 Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al 发布。
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1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (来自 Google AI) 伴随论文 [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) 由 Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby 发布。
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1. **[OWLv2](https://huggingface.co/docs/transformers/main/model_doc/owlv2)** (来自 Google AI) 伴随论文 [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) 由 Matthias Minderer, Alexey Gritsenko, Neil Houlsby 发布。
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1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (来自 Google) 伴随论文 [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) 由 Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu 发布。
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1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (来自 Google) 伴随论文 [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347) 由 Jason Phang, Yao Zhao, Peter J. Liu 发布。
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1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (来自 Deepmind) 伴随论文 [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) 由 Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira 发布。
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@@ -394,6 +394,7 @@ conda install -c huggingface transformers
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1. **[OpenLlama](https://huggingface.co/docs/transformers/model_doc/open-llama)** (from [s-JoL](https://huggingface.co/s-JoL)) released in [Open-Llama](https://github.com/s-JoL/Open-Llama).
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1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
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1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (from Google AI) released with the paper [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby.
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1. **[OWLv2](https://huggingface.co/docs/transformers/main/model_doc/owlv2)** (from Google AI) released with the paper [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) by Matthias Minderer, Alexey Gritsenko, Neil Houlsby.
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1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
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1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (from Google) released with the paper [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347) by Jason Phang, Yao Zhao, Peter J. Liu.
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1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
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@@ -695,6 +695,8 @@
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title: OneFormer
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- local: model_doc/owlvit
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title: OWL-ViT
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- local: model_doc/owlv2
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title: OWLv2
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- local: model_doc/perceiver
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title: Perceiver
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- local: model_doc/pix2struct
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@@ -209,6 +209,7 @@ Flax), PyTorch, and/or TensorFlow.
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| [OpenLlama](model_doc/open-llama) | ✅ | ❌ | ❌ |
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| [OPT](model_doc/opt) | ✅ | ✅ | ✅ |
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| [OWL-ViT](model_doc/owlvit) | ✅ | ❌ | ❌ |
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| [OWLv2](model_doc/owlv2) | ✅ | ❌ | ❌ |
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| [Pegasus](model_doc/pegasus) | ✅ | ✅ | ✅ |
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| [PEGASUS-X](model_doc/pegasus_x) | ✅ | ❌ | ❌ |
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| [Perceiver](model_doc/perceiver) | ✅ | ❌ | ❌ |
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docs/source/en/model_doc/owlv2.md
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docs/source/en/model_doc/owlv2.md
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@@ -0,0 +1,117 @@
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<!--Copyright 2023 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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# OWLv2
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## Overview
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OWLv2 was proposed in [Scaling Open-Vocabulary Object Detection](https://arxiv.org/abs/2306.09683) by Matthias Minderer, Alexey Gritsenko, Neil Houlsby. OWLv2 scales up [OWL-ViT](owlvit) using self-training, which uses an existing detector to generate pseudo-box annotations on image-text pairs. This results in large gains over the previous state-of-the-art for zero-shot object detection.
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The abstract from the paper is the following:
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|
||||
*Open-vocabulary object detection has benefited greatly from pretrained vision-language models, but is still limited by the amount of available detection training data. While detection training data can be expanded by using Web image-text pairs as weak supervision, this has not been done at scales comparable to image-level pretraining. Here, we scale up detection data with self-training, which uses an existing detector to generate pseudo-box annotations on image-text pairs. Major challenges in scaling self-training are the choice of label space, pseudo-annotation filtering, and training efficiency. We present the OWLv2 model and OWL-ST self-training recipe, which address these challenges. OWLv2 surpasses the performance of previous state-of-the-art open-vocabulary detectors already at comparable training scales (~10M examples). However, with OWL-ST, we can scale to over 1B examples, yielding further large improvement: With an L/14 architecture, OWL-ST improves AP on LVIS rare classes, for which the model has seen no human box annotations, from 31.2% to 44.6% (43% relative improvement). OWL-ST unlocks Web-scale training for open-world localization, similar to what has been seen for image classification and language modelling.*
|
||||
|
||||
Tips:
|
||||
|
||||
- The architecture of OWLv2 is identical to [OWL-ViT](owlvit), however the object detection head now also includes an objectness classifier, which predicts the (query-agnostic) likelihood that a predicted box contains an object (as opposed to background). The objectness score can be used to rank or filter predictions independently of text queries.
|
||||
- Usage of OWLv2 is identical to [OWL-ViT](owlvit) with a new, updated image processor ([`Owlv2ImageProcessor`]).
|
||||
|
||||
This model was contributed by [nielsr](https://huggingface.co/nielsr).
|
||||
The original code can be found [here](https://github.com/google-research/scenic/tree/main/scenic/projects/owl_vit).
|
||||
|
||||
## Usage
|
||||
|
||||
OWL-ViT a zero-shot text-conditioned object detection model. OWL-ViT uses [CLIP](clip) as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features. To use CLIP for detection, OWL-ViT removes the final token pooling layer of the vision model and attaches a lightweight classification and box head to each transformer output token. Open-vocabulary classification is enabled by replacing the fixed classification layer weights with the class-name embeddings obtained from the text model. The authors first train CLIP from scratch and fine-tune it end-to-end with the classification and box heads on standard detection datasets using a bipartite matching loss. One or multiple text queries per image can be used to perform zero-shot text-conditioned object detection.
|
||||
|
||||
[`Owlv2ImageProcessor`] can be used to resize (or rescale) and normalize images for the model and [`CLIPTokenizer`] is used to encode the text. [`Owlv2Processor`] wraps [`Owlv2ImageProcessor`] and [`CLIPTokenizer`] into a single instance to both encode the text and prepare the images. The following example shows how to perform object detection using [`Owlv2Processor`] and [`Owlv2ForObjectDetection`].
|
||||
|
||||
|
||||
```python
|
||||
>>> import requests
|
||||
>>> from PIL import Image
|
||||
>>> import torch
|
||||
|
||||
>>> from transformers import Owlv2Processor, Owlv2ForObjectDetection
|
||||
|
||||
>>> processor = Owlv2Processor.from_pretrained("google/owlv2-base-patch16-ensemble")
|
||||
>>> model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble")
|
||||
|
||||
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
||||
>>> image = Image.open(requests.get(url, stream=True).raw)
|
||||
>>> texts = [["a photo of a cat", "a photo of a dog"]]
|
||||
>>> inputs = processor(text=texts, images=image, return_tensors="pt")
|
||||
>>> outputs = model(**inputs)
|
||||
|
||||
>>> # Target image sizes (height, width) to rescale box predictions [batch_size, 2]
|
||||
>>> target_sizes = torch.Tensor([image.size[::-1]])
|
||||
>>> # Convert outputs (bounding boxes and class logits) to COCO API
|
||||
>>> results = processor.post_process_object_detection(outputs=outputs, target_sizes=target_sizes, threshold=0.1)
|
||||
>>> i = 0 # Retrieve predictions for the first image for the corresponding text queries
|
||||
>>> text = texts[i]
|
||||
>>> boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"]
|
||||
>>> for box, score, label in zip(boxes, scores, labels):
|
||||
... box = [round(i, 2) for i in box.tolist()]
|
||||
... print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}")
|
||||
Detected a photo of a cat with confidence 0.614 at location [341.67, 17.54, 642.32, 278.51]
|
||||
Detected a photo of a cat with confidence 0.665 at location [6.75, 38.97, 326.62, 354.85]
|
||||
```
|
||||
|
||||
## Owlv2Config
|
||||
|
||||
[[autodoc]] Owlv2Config
|
||||
- from_text_vision_configs
|
||||
|
||||
## Owlv2TextConfig
|
||||
|
||||
[[autodoc]] Owlv2TextConfig
|
||||
|
||||
## Owlv2VisionConfig
|
||||
|
||||
[[autodoc]] Owlv2VisionConfig
|
||||
|
||||
## Owlv2ImageProcessor
|
||||
|
||||
[[autodoc]] Owlv2ImageProcessor
|
||||
- preprocess
|
||||
- post_process_object_detection
|
||||
- post_process_image_guided_detection
|
||||
|
||||
## Owlv2Processor
|
||||
|
||||
[[autodoc]] Owlv2Processor
|
||||
|
||||
## Owlv2Model
|
||||
|
||||
[[autodoc]] Owlv2Model
|
||||
- forward
|
||||
- get_text_features
|
||||
- get_image_features
|
||||
|
||||
## Owlv2TextModel
|
||||
|
||||
[[autodoc]] Owlv2TextModel
|
||||
- forward
|
||||
|
||||
## Owlv2VisionModel
|
||||
|
||||
[[autodoc]] Owlv2VisionModel
|
||||
- forward
|
||||
|
||||
## Owlv2ForObjectDetection
|
||||
|
||||
[[autodoc]] Owlv2ForObjectDetection
|
||||
- forward
|
||||
- image_guided_detection
|
||||
@@ -462,6 +462,13 @@ _import_structure = {
|
||||
"models.oneformer": ["ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "OneFormerConfig", "OneFormerProcessor"],
|
||||
"models.openai": ["OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenAIGPTConfig", "OpenAIGPTTokenizer"],
|
||||
"models.opt": ["OPTConfig"],
|
||||
"models.owlv2": [
|
||||
"OWLV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
|
||||
"Owlv2Config",
|
||||
"Owlv2Processor",
|
||||
"Owlv2TextConfig",
|
||||
"Owlv2VisionConfig",
|
||||
],
|
||||
"models.owlvit": [
|
||||
"OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
|
||||
"OwlViTConfig",
|
||||
@@ -978,6 +985,7 @@ else:
|
||||
_import_structure["models.mobilevit"].extend(["MobileViTFeatureExtractor", "MobileViTImageProcessor"])
|
||||
_import_structure["models.nougat"].append("NougatImageProcessor")
|
||||
_import_structure["models.oneformer"].extend(["OneFormerImageProcessor"])
|
||||
_import_structure["models.owlv2"].append("Owlv2ImageProcessor")
|
||||
_import_structure["models.owlvit"].extend(["OwlViTFeatureExtractor", "OwlViTImageProcessor"])
|
||||
_import_structure["models.perceiver"].extend(["PerceiverFeatureExtractor", "PerceiverImageProcessor"])
|
||||
_import_structure["models.pix2struct"].extend(["Pix2StructImageProcessor"])
|
||||
@@ -2421,6 +2429,16 @@ else:
|
||||
"OPTPreTrainedModel",
|
||||
]
|
||||
)
|
||||
_import_structure["models.owlv2"].extend(
|
||||
[
|
||||
"OWLV2_PRETRAINED_MODEL_ARCHIVE_LIST",
|
||||
"Owlv2ForObjectDetection",
|
||||
"Owlv2Model",
|
||||
"Owlv2PreTrainedModel",
|
||||
"Owlv2TextModel",
|
||||
"Owlv2VisionModel",
|
||||
]
|
||||
)
|
||||
_import_structure["models.owlvit"].extend(
|
||||
[
|
||||
"OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
|
||||
@@ -4579,6 +4597,13 @@ if TYPE_CHECKING:
|
||||
from .models.oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig, OneFormerProcessor
|
||||
from .models.openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig, OpenAIGPTTokenizer
|
||||
from .models.opt import OPTConfig
|
||||
from .models.owlv2 import (
|
||||
OWLV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
|
||||
Owlv2Config,
|
||||
Owlv2Processor,
|
||||
Owlv2TextConfig,
|
||||
Owlv2VisionConfig,
|
||||
)
|
||||
from .models.owlvit import (
|
||||
OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
|
||||
OwlViTConfig,
|
||||
@@ -5041,6 +5066,7 @@ if TYPE_CHECKING:
|
||||
from .models.mobilevit import MobileViTFeatureExtractor, MobileViTImageProcessor
|
||||
from .models.nougat import NougatImageProcessor
|
||||
from .models.oneformer import OneFormerImageProcessor
|
||||
from .models.owlv2 import Owlv2ImageProcessor
|
||||
from .models.owlvit import OwlViTFeatureExtractor, OwlViTImageProcessor
|
||||
from .models.perceiver import PerceiverFeatureExtractor, PerceiverImageProcessor
|
||||
from .models.pix2struct import Pix2StructImageProcessor
|
||||
@@ -6239,6 +6265,14 @@ if TYPE_CHECKING:
|
||||
OPTModel,
|
||||
OPTPreTrainedModel,
|
||||
)
|
||||
from .models.owlv2 import (
|
||||
OWLV2_PRETRAINED_MODEL_ARCHIVE_LIST,
|
||||
Owlv2ForObjectDetection,
|
||||
Owlv2Model,
|
||||
Owlv2PreTrainedModel,
|
||||
Owlv2TextModel,
|
||||
Owlv2VisionModel,
|
||||
)
|
||||
from .models.owlvit import (
|
||||
OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
|
||||
OwlViTForObjectDetection,
|
||||
|
||||
@@ -153,6 +153,7 @@ from . import (
|
||||
oneformer,
|
||||
openai,
|
||||
opt,
|
||||
owlv2,
|
||||
owlvit,
|
||||
pegasus,
|
||||
pegasus_x,
|
||||
|
||||
@@ -159,6 +159,7 @@ CONFIG_MAPPING_NAMES = OrderedDict(
|
||||
("open-llama", "OpenLlamaConfig"),
|
||||
("openai-gpt", "OpenAIGPTConfig"),
|
||||
("opt", "OPTConfig"),
|
||||
("owlv2", "Owlv2Config"),
|
||||
("owlvit", "OwlViTConfig"),
|
||||
("pegasus", "PegasusConfig"),
|
||||
("pegasus_x", "PegasusXConfig"),
|
||||
@@ -365,6 +366,7 @@ CONFIG_ARCHIVE_MAP_MAPPING_NAMES = OrderedDict(
|
||||
("open-llama", "OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("openai-gpt", "OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("opt", "OPT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("owlv2", "OWLV2_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("owlvit", "OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("pegasus", "PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
("pegasus_x", "PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP"),
|
||||
@@ -590,6 +592,7 @@ MODEL_NAMES_MAPPING = OrderedDict(
|
||||
("open-llama", "OpenLlama"),
|
||||
("openai-gpt", "OpenAI GPT"),
|
||||
("opt", "OPT"),
|
||||
("owlv2", "OWLv2"),
|
||||
("owlvit", "OWL-ViT"),
|
||||
("pegasus", "Pegasus"),
|
||||
("pegasus_x", "PEGASUS-X"),
|
||||
|
||||
@@ -84,6 +84,7 @@ IMAGE_PROCESSOR_MAPPING_NAMES = OrderedDict(
|
||||
("nat", "ViTImageProcessor"),
|
||||
("nougat", "NougatImageProcessor"),
|
||||
("oneformer", "OneFormerImageProcessor"),
|
||||
("owlv2", "Owlv2ImageProcessor"),
|
||||
("owlvit", "OwlViTImageProcessor"),
|
||||
("perceiver", "PerceiverImageProcessor"),
|
||||
("pix2struct", "Pix2StructImageProcessor"),
|
||||
|
||||
@@ -153,6 +153,7 @@ MODEL_MAPPING_NAMES = OrderedDict(
|
||||
("open-llama", "OpenLlamaModel"),
|
||||
("openai-gpt", "OpenAIGPTModel"),
|
||||
("opt", "OPTModel"),
|
||||
("owlv2", "Owlv2Model"),
|
||||
("owlvit", "OwlViTModel"),
|
||||
("pegasus", "PegasusModel"),
|
||||
("pegasus_x", "PegasusXModel"),
|
||||
@@ -638,7 +639,8 @@ MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES = OrderedDict(
|
||||
MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES = OrderedDict(
|
||||
[
|
||||
# Model for Zero Shot Object Detection mapping
|
||||
("owlvit", "OwlViTForObjectDetection")
|
||||
("owlv2", "Owlv2ForObjectDetection"),
|
||||
("owlvit", "OwlViTForObjectDetection"),
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
@@ -65,6 +65,7 @@ PROCESSOR_MAPPING_NAMES = OrderedDict(
|
||||
("mctct", "MCTCTProcessor"),
|
||||
("mgp-str", "MgpstrProcessor"),
|
||||
("oneformer", "OneFormerProcessor"),
|
||||
("owlv2", "Owlv2Processor"),
|
||||
("owlvit", "OwlViTProcessor"),
|
||||
("pix2struct", "Pix2StructProcessor"),
|
||||
("pop2piano", "Pop2PianoProcessor"),
|
||||
|
||||
@@ -269,6 +269,7 @@ else:
|
||||
("oneformer", ("CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None)),
|
||||
("openai-gpt", ("OpenAIGPTTokenizer", "OpenAIGPTTokenizerFast" if is_tokenizers_available() else None)),
|
||||
("opt", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)),
|
||||
("owlv2", ("CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None)),
|
||||
("owlvit", ("CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None)),
|
||||
(
|
||||
"pegasus",
|
||||
|
||||
93
src/transformers/models/owlv2/__init__.py
Normal file
93
src/transformers/models/owlv2/__init__.py
Normal file
@@ -0,0 +1,93 @@
|
||||
# Copyright 2023 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_owlv2": [
|
||||
"OWLV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
|
||||
"Owlv2Config",
|
||||
"Owlv2TextConfig",
|
||||
"Owlv2VisionConfig",
|
||||
],
|
||||
"processing_owlv2": ["Owlv2Processor"],
|
||||
}
|
||||
|
||||
try:
|
||||
if not is_vision_available():
|
||||
raise OptionalDependencyNotAvailable()
|
||||
except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
_import_structure["image_processing_owlv2"] = ["Owlv2ImageProcessor"]
|
||||
|
||||
|
||||
try:
|
||||
if not is_torch_available():
|
||||
raise OptionalDependencyNotAvailable()
|
||||
except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
_import_structure["modeling_owlv2"] = [
|
||||
"OWLV2_PRETRAINED_MODEL_ARCHIVE_LIST",
|
||||
"Owlv2Model",
|
||||
"Owlv2PreTrainedModel",
|
||||
"Owlv2TextModel",
|
||||
"Owlv2VisionModel",
|
||||
"Owlv2ForObjectDetection",
|
||||
]
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from .configuration_owlv2 import (
|
||||
OWLV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
|
||||
Owlv2Config,
|
||||
Owlv2TextConfig,
|
||||
Owlv2VisionConfig,
|
||||
)
|
||||
from .processing_owlv2 import Owlv2Processor
|
||||
|
||||
try:
|
||||
if not is_vision_available():
|
||||
raise OptionalDependencyNotAvailable()
|
||||
except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
from .image_processing_owlv2 import Owlv2ImageProcessor
|
||||
|
||||
try:
|
||||
if not is_torch_available():
|
||||
raise OptionalDependencyNotAvailable()
|
||||
except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
from .modeling_owlv2 import (
|
||||
OWLV2_PRETRAINED_MODEL_ARCHIVE_LIST,
|
||||
Owlv2ForObjectDetection,
|
||||
Owlv2Model,
|
||||
Owlv2PreTrainedModel,
|
||||
Owlv2TextModel,
|
||||
Owlv2VisionModel,
|
||||
)
|
||||
|
||||
else:
|
||||
import sys
|
||||
|
||||
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
|
||||
336
src/transformers/models/owlv2/configuration_owlv2.py
Normal file
336
src/transformers/models/owlv2/configuration_owlv2.py
Normal file
@@ -0,0 +1,336 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2023 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.
|
||||
""" OWLv2 model configuration"""
|
||||
|
||||
import os
|
||||
from typing import TYPE_CHECKING, Dict, Union
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
pass
|
||||
|
||||
from ...configuration_utils import PretrainedConfig
|
||||
from ...utils import logging
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
OWLV2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
|
||||
"google/owlv2-base-patch16": "https://huggingface.co/google/owlv2-base-patch16/resolve/main/config.json",
|
||||
}
|
||||
|
||||
|
||||
# Copied from transformers.models.owlvit.configuration_owlvit.OwlViTTextConfig with OwlViT->Owlv2, owlvit-base-patch-16->owlv2-base-patch16, owlvit->owlv2, OWL-ViT->OWLv2
|
||||
class Owlv2TextConfig(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of an [`Owlv2TextModel`]. It is used to instantiate an
|
||||
Owlv2 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 Owlv2
|
||||
[google/owlv2-base-patch32](https://huggingface.co/google/owlv2-base-patch32) 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 49408):
|
||||
Vocabulary size of the OWLv2 text model. Defines the number of different tokens that can be represented by
|
||||
the `inputs_ids` passed when calling [`Owlv2TextModel`].
|
||||
hidden_size (`int`, *optional*, defaults to 512):
|
||||
Dimensionality of the encoder layers and the pooler layer.
|
||||
intermediate_size (`int`, *optional*, defaults to 2048):
|
||||
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 8):
|
||||
Number of attention heads for each attention layer in the Transformer encoder.
|
||||
max_position_embeddings (`int`, *optional*, defaults to 16):
|
||||
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 `"quick_gelu"`):
|
||||
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-05):
|
||||
The epsilon used by the layer normalization layers.
|
||||
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||
The dropout ratio for the attention probabilities.
|
||||
initializer_range (`float`, *optional*, defaults to 0.02):
|
||||
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||||
initializer_factor (`float`, *optional*, defaults to 1.0):
|
||||
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
|
||||
testing).
|
||||
pad_token_id (`int`, *optional*, defaults to 0):
|
||||
The id of the padding token in the input sequences.
|
||||
bos_token_id (`int`, *optional*, defaults to 49406):
|
||||
The id of the beginning-of-sequence token in the input sequences.
|
||||
eos_token_id (`int`, *optional*, defaults to 49407):
|
||||
The id of the end-of-sequence token in the input sequences.
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
>>> from transformers import Owlv2TextConfig, Owlv2TextModel
|
||||
|
||||
>>> # Initializing a Owlv2TextModel with google/owlv2-base-patch32 style configuration
|
||||
>>> configuration = Owlv2TextConfig()
|
||||
|
||||
>>> # Initializing a Owlv2TextConfig from the google/owlv2-base-patch32 style configuration
|
||||
>>> model = Owlv2TextModel(configuration)
|
||||
|
||||
>>> # Accessing the model configuration
|
||||
>>> configuration = model.config
|
||||
```"""
|
||||
model_type = "owlv2_text_model"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_size=49408,
|
||||
hidden_size=512,
|
||||
intermediate_size=2048,
|
||||
num_hidden_layers=12,
|
||||
num_attention_heads=8,
|
||||
max_position_embeddings=16,
|
||||
hidden_act="quick_gelu",
|
||||
layer_norm_eps=1e-5,
|
||||
attention_dropout=0.0,
|
||||
initializer_range=0.02,
|
||||
initializer_factor=1.0,
|
||||
pad_token_id=0,
|
||||
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.hidden_act = hidden_act
|
||||
self.layer_norm_eps = layer_norm_eps
|
||||
self.attention_dropout = attention_dropout
|
||||
self.initializer_range = initializer_range
|
||||
self.initializer_factor = initializer_factor
|
||||
|
||||
@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 Owlv2Config
|
||||
if config_dict.get("model_type") == "owlv2":
|
||||
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)
|
||||
|
||||
|
||||
# Copied from transformers.models.owlvit.configuration_owlvit.OwlViTVisionConfig with OwlViT->Owlv2, owlvit-base-patch-32->owlv2-base-patch16, owlvit->owlv2, OWL-ViT->OWLv2
|
||||
class Owlv2VisionConfig(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of an [`Owlv2VisionModel`]. It is used to instantiate an
|
||||
OWLv2 image 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 OWLv2
|
||||
[google/owlv2-base-patch32](https://huggingface.co/google/owlv2-base-patch32) 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 768):
|
||||
The size (resolution) of each image.
|
||||
patch_size (`int`, *optional*, defaults to 32):
|
||||
The size (resolution) of each patch.
|
||||
hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
|
||||
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-05):
|
||||
The epsilon used by the layer normalization layers.
|
||||
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||
The dropout ratio for the attention probabilities.
|
||||
initializer_range (`float`, *optional*, defaults to 0.02):
|
||||
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||||
initializer_factor (`float``, *optional*, defaults to 1.0):
|
||||
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
|
||||
testing).
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
>>> from transformers import Owlv2VisionConfig, Owlv2VisionModel
|
||||
|
||||
>>> # Initializing a Owlv2VisionModel with google/owlv2-base-patch32 style configuration
|
||||
>>> configuration = Owlv2VisionConfig()
|
||||
|
||||
>>> # Initializing a Owlv2VisionModel model from the google/owlv2-base-patch32 style configuration
|
||||
>>> model = Owlv2VisionModel(configuration)
|
||||
|
||||
>>> # Accessing the model configuration
|
||||
>>> configuration = model.config
|
||||
```"""
|
||||
|
||||
model_type = "owlv2_vision_model"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
hidden_size=768,
|
||||
intermediate_size=3072,
|
||||
num_hidden_layers=12,
|
||||
num_attention_heads=12,
|
||||
num_channels=3,
|
||||
image_size=768,
|
||||
patch_size=32,
|
||||
hidden_act="quick_gelu",
|
||||
layer_norm_eps=1e-5,
|
||||
attention_dropout=0.0,
|
||||
initializer_range=0.02,
|
||||
initializer_factor=1.0,
|
||||
**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.image_size = image_size
|
||||
self.patch_size = patch_size
|
||||
self.hidden_act = hidden_act
|
||||
self.layer_norm_eps = layer_norm_eps
|
||||
self.attention_dropout = attention_dropout
|
||||
self.initializer_range = initializer_range
|
||||
self.initializer_factor = initializer_factor
|
||||
|
||||
@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 Owlv2Config
|
||||
if config_dict.get("model_type") == "owlv2":
|
||||
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)
|
||||
|
||||
|
||||
# Copied from transformers.models.owlvit.configuration_owlvit.OwlViTConfig with OwlViT->Owlv2, owlvit-base-patch-32->owlv2-base-patch32, owlvit->owlv2, OWL-ViT->OWLv2
|
||||
class Owlv2Config(PretrainedConfig):
|
||||
r"""
|
||||
[`Owlv2Config`] is the configuration class to store the configuration of an [`Owlv2Model`]. It is used to
|
||||
instantiate an OWLv2 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 OWLv2
|
||||
[google/owlv2-base-patch32](https://huggingface.co/google/owlv2-base-patch32) 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 [`Owlv2TextConfig`].
|
||||
vision_config (`dict`, *optional*):
|
||||
Dictionary of configuration options used to initialize [`Owlv2VisionConfig`].
|
||||
projection_dim (`int`, *optional*, defaults to 512):
|
||||
Dimensionality of text and vision projection layers.
|
||||
logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
|
||||
The inital value of the *logit_scale* parameter. Default is used as per the original OWLv2 implementation.
|
||||
return_dict (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not the model should return a dictionary. If `False`, returns a tuple.
|
||||
kwargs (*optional*):
|
||||
Dictionary of keyword arguments.
|
||||
"""
|
||||
|
||||
model_type = "owlv2"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
text_config=None,
|
||||
vision_config=None,
|
||||
projection_dim=512,
|
||||
logit_scale_init_value=2.6592,
|
||||
return_dict=True,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
if text_config is None:
|
||||
text_config = {}
|
||||
logger.info("text_config is None. Initializing the Owlv2TextConfig with default values.")
|
||||
|
||||
if vision_config is None:
|
||||
vision_config = {}
|
||||
logger.info("vision_config is None. initializing the Owlv2VisionConfig with default values.")
|
||||
|
||||
self.text_config = Owlv2TextConfig(**text_config)
|
||||
self.vision_config = Owlv2VisionConfig(**vision_config)
|
||||
|
||||
self.projection_dim = projection_dim
|
||||
self.logit_scale_init_value = logit_scale_init_value
|
||||
self.return_dict = return_dict
|
||||
self.initializer_factor = 1.0
|
||||
|
||||
@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)
|
||||
|
||||
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)
|
||||
|
||||
@classmethod
|
||||
def from_text_vision_configs(cls, text_config: Dict, vision_config: Dict, **kwargs):
|
||||
r"""
|
||||
Instantiate a [`Owlv2Config`] (or a derived class) from owlv2 text model configuration and owlv2 vision model
|
||||
configuration.
|
||||
|
||||
Returns:
|
||||
[`Owlv2Config`]: An instance of a configuration object
|
||||
"""
|
||||
config_dict = {}
|
||||
config_dict["text_config"] = text_config
|
||||
config_dict["vision_config"] = vision_config
|
||||
|
||||
return cls.from_dict(config_dict, **kwargs)
|
||||
422
src/transformers/models/owlv2/convert_owlv2_to_hf.py
Normal file
422
src/transformers/models/owlv2/convert_owlv2_to_hf.py
Normal file
@@ -0,0 +1,422 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2023 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 OWLv2 checkpoints from the original repository.
|
||||
|
||||
URL: https://github.com/google-research/scenic/tree/main/scenic/projects/owl_vit"""
|
||||
|
||||
import argparse
|
||||
import collections
|
||||
import os
|
||||
|
||||
import jax
|
||||
import jax.numpy as jnp
|
||||
import numpy as np
|
||||
import torch
|
||||
from flax.training import checkpoints
|
||||
from huggingface_hub import hf_hub_download
|
||||
from PIL import Image
|
||||
|
||||
from transformers import (
|
||||
CLIPTokenizer,
|
||||
Owlv2Config,
|
||||
Owlv2ForObjectDetection,
|
||||
Owlv2ImageProcessor,
|
||||
Owlv2Processor,
|
||||
Owlv2TextConfig,
|
||||
Owlv2VisionConfig,
|
||||
)
|
||||
from transformers.utils import logging
|
||||
|
||||
|
||||
logging.set_verbosity_info()
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
def get_owlv2_config(model_name):
|
||||
if "large" in model_name:
|
||||
image_size = 1008
|
||||
patch_size = 14
|
||||
vision_hidden_size = 1024
|
||||
vision_intermediate_size = 4096
|
||||
vision_num_hidden_layers = 24
|
||||
vision_num_attention_heads = 16
|
||||
projection_dim = 768
|
||||
text_hidden_size = 768
|
||||
text_intermediate_size = 3072
|
||||
text_num_attention_heads = 12
|
||||
text_num_hidden_layers = 12
|
||||
else:
|
||||
image_size = 960
|
||||
patch_size = 16
|
||||
vision_hidden_size = 768
|
||||
vision_intermediate_size = 3072
|
||||
vision_num_hidden_layers = 12
|
||||
vision_num_attention_heads = 12
|
||||
projection_dim = 512
|
||||
text_hidden_size = 512
|
||||
text_intermediate_size = 2048
|
||||
text_num_attention_heads = 8
|
||||
text_num_hidden_layers = 12
|
||||
|
||||
vision_config = Owlv2VisionConfig(
|
||||
patch_size=patch_size,
|
||||
image_size=image_size,
|
||||
hidden_size=vision_hidden_size,
|
||||
num_hidden_layers=vision_num_hidden_layers,
|
||||
intermediate_size=vision_intermediate_size,
|
||||
num_attention_heads=vision_num_attention_heads,
|
||||
)
|
||||
text_config = Owlv2TextConfig(
|
||||
hidden_size=text_hidden_size,
|
||||
intermediate_size=text_intermediate_size,
|
||||
num_attention_heads=text_num_attention_heads,
|
||||
num_hidden_layers=text_num_hidden_layers,
|
||||
)
|
||||
|
||||
config = Owlv2Config(
|
||||
text_config=text_config.to_dict(),
|
||||
vision_config=vision_config.to_dict(),
|
||||
projection_dim=projection_dim,
|
||||
)
|
||||
|
||||
return config
|
||||
|
||||
|
||||
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.MutableMapping):
|
||||
items.extend(flatten_nested_dict(v, new_key, sep=sep).items())
|
||||
else:
|
||||
items.append((new_key, v))
|
||||
return dict(items)
|
||||
|
||||
|
||||
# here we list all keys to be renamed (original name on the left, our name on the right)
|
||||
def create_rename_keys(config, model_name):
|
||||
rename_keys = []
|
||||
|
||||
# fmt: off
|
||||
# CLIP vision encoder
|
||||
rename_keys.append(("backbone/clip/visual/class_embedding", "owlv2.vision_model.embeddings.class_embedding"))
|
||||
rename_keys.append(("backbone/clip/visual/conv1/kernel", "owlv2.vision_model.embeddings.patch_embedding.weight"))
|
||||
rename_keys.append(("backbone/clip/visual/positional_embedding", "owlv2.vision_model.embeddings.position_embedding.weight"))
|
||||
rename_keys.append(("backbone/clip/visual/ln_pre/scale", "owlv2.vision_model.pre_layernorm.weight"))
|
||||
rename_keys.append(("backbone/clip/visual/ln_pre/bias", "owlv2.vision_model.pre_layernorm.bias"))
|
||||
|
||||
for i in range(config.vision_config.num_hidden_layers):
|
||||
if "v2" in model_name:
|
||||
rename_keys.append((f"backbone/clip/visual/transformer/resblocks.{i}/ln_0/scale", f"owlv2.vision_model.encoder.layers.{i}.layer_norm1.weight"))
|
||||
rename_keys.append((f"backbone/clip/visual/transformer/resblocks.{i}/ln_0/bias", f"owlv2.vision_model.encoder.layers.{i}.layer_norm1.bias"))
|
||||
rename_keys.append((f"backbone/clip/visual/transformer/resblocks.{i}/ln_1/scale", f"owlv2.vision_model.encoder.layers.{i}.layer_norm2.weight"))
|
||||
rename_keys.append((f"backbone/clip/visual/transformer/resblocks.{i}/ln_1/bias", f"owlv2.vision_model.encoder.layers.{i}.layer_norm2.bias"))
|
||||
else:
|
||||
rename_keys.append((f"backbone/clip/visual/transformer/resblocks.{i}/ln_1/scale", f"owlv2.vision_model.encoder.layers.{i}.layer_norm1.weight"))
|
||||
rename_keys.append((f"backbone/clip/visual/transformer/resblocks.{i}/ln_1/bias", f"owlv2.vision_model.encoder.layers.{i}.layer_norm1.bias"))
|
||||
rename_keys.append((f"backbone/clip/visual/transformer/resblocks.{i}/ln_2/scale", f"owlv2.vision_model.encoder.layers.{i}.layer_norm2.weight"))
|
||||
rename_keys.append((f"backbone/clip/visual/transformer/resblocks.{i}/ln_2/bias", f"owlv2.vision_model.encoder.layers.{i}.layer_norm2.bias"))
|
||||
rename_keys.append((f"backbone/clip/visual/transformer/resblocks.{i}/mlp/c_fc/kernel", f"owlv2.vision_model.encoder.layers.{i}.mlp.fc1.weight"))
|
||||
rename_keys.append((f"backbone/clip/visual/transformer/resblocks.{i}/mlp/c_fc/bias", f"owlv2.vision_model.encoder.layers.{i}.mlp.fc1.bias"))
|
||||
rename_keys.append((f"backbone/clip/visual/transformer/resblocks.{i}/mlp/c_proj/kernel", f"owlv2.vision_model.encoder.layers.{i}.mlp.fc2.weight"))
|
||||
rename_keys.append((f"backbone/clip/visual/transformer/resblocks.{i}/mlp/c_proj/bias", f"owlv2.vision_model.encoder.layers.{i}.mlp.fc2.bias"))
|
||||
rename_keys.append((f"backbone/clip/visual/transformer/resblocks.{i}/attn/query/kernel", f"owlv2.vision_model.encoder.layers.{i}.self_attn.q_proj.weight"))
|
||||
rename_keys.append((f"backbone/clip/visual/transformer/resblocks.{i}/attn/query/bias", f"owlv2.vision_model.encoder.layers.{i}.self_attn.q_proj.bias"))
|
||||
rename_keys.append((f"backbone/clip/visual/transformer/resblocks.{i}/attn/key/kernel", f"owlv2.vision_model.encoder.layers.{i}.self_attn.k_proj.weight"))
|
||||
rename_keys.append((f"backbone/clip/visual/transformer/resblocks.{i}/attn/key/bias", f"owlv2.vision_model.encoder.layers.{i}.self_attn.k_proj.bias"))
|
||||
rename_keys.append((f"backbone/clip/visual/transformer/resblocks.{i}/attn/value/kernel", f"owlv2.vision_model.encoder.layers.{i}.self_attn.v_proj.weight"))
|
||||
rename_keys.append((f"backbone/clip/visual/transformer/resblocks.{i}/attn/value/bias", f"owlv2.vision_model.encoder.layers.{i}.self_attn.v_proj.bias"))
|
||||
rename_keys.append((f"backbone/clip/visual/transformer/resblocks.{i}/attn/out/kernel", f"owlv2.vision_model.encoder.layers.{i}.self_attn.out_proj.weight"))
|
||||
rename_keys.append((f"backbone/clip/visual/transformer/resblocks.{i}/attn/out/bias", f"owlv2.vision_model.encoder.layers.{i}.self_attn.out_proj.bias"))
|
||||
|
||||
rename_keys.append(("backbone/clip/visual/ln_post/scale", "owlv2.vision_model.post_layernorm.weight"))
|
||||
rename_keys.append(("backbone/clip/visual/ln_post/bias", "owlv2.vision_model.post_layernorm.bias"))
|
||||
|
||||
# CLIP text encoder
|
||||
rename_keys.append(("backbone/clip/text/token_embedding/embedding", "owlv2.text_model.embeddings.token_embedding.weight"))
|
||||
rename_keys.append(("backbone/clip/text/positional_embedding", "owlv2.text_model.embeddings.position_embedding.weight"))
|
||||
|
||||
for i in range(config.text_config.num_hidden_layers):
|
||||
if "v2" in model_name:
|
||||
rename_keys.append((f"backbone/clip/text/transformer/resblocks.{i}/ln_0/scale", f"owlv2.text_model.encoder.layers.{i}.layer_norm1.weight"))
|
||||
rename_keys.append((f"backbone/clip/text/transformer/resblocks.{i}/ln_0/bias", f"owlv2.text_model.encoder.layers.{i}.layer_norm1.bias"))
|
||||
rename_keys.append((f"backbone/clip/text/transformer/resblocks.{i}/ln_1/scale", f"owlv2.text_model.encoder.layers.{i}.layer_norm2.weight"))
|
||||
rename_keys.append((f"backbone/clip/text/transformer/resblocks.{i}/ln_1/bias", f"owlv2.text_model.encoder.layers.{i}.layer_norm2.bias"))
|
||||
else:
|
||||
rename_keys.append((f"backbone/clip/text/transformer/resblocks.{i}/ln_1/scale", f"owlv2.text_model.encoder.layers.{i}.layer_norm1.weight"))
|
||||
rename_keys.append((f"backbone/clip/text/transformer/resblocks.{i}/ln_1/bias", f"owlv2.text_model.encoder.layers.{i}.layer_norm1.bias"))
|
||||
rename_keys.append((f"backbone/clip/text/transformer/resblocks.{i}/ln_2/scale", f"owlv2.text_model.encoder.layers.{i}.layer_norm2.weight"))
|
||||
rename_keys.append((f"backbone/clip/text/transformer/resblocks.{i}/ln_2/bias", f"owlv2.text_model.encoder.layers.{i}.layer_norm2.bias"))
|
||||
rename_keys.append((f"backbone/clip/text/transformer/resblocks.{i}/mlp/c_fc/kernel", f"owlv2.text_model.encoder.layers.{i}.mlp.fc1.weight"))
|
||||
rename_keys.append((f"backbone/clip/text/transformer/resblocks.{i}/mlp/c_fc/bias", f"owlv2.text_model.encoder.layers.{i}.mlp.fc1.bias"))
|
||||
rename_keys.append((f"backbone/clip/text/transformer/resblocks.{i}/mlp/c_proj/kernel", f"owlv2.text_model.encoder.layers.{i}.mlp.fc2.weight"))
|
||||
rename_keys.append((f"backbone/clip/text/transformer/resblocks.{i}/mlp/c_proj/bias", f"owlv2.text_model.encoder.layers.{i}.mlp.fc2.bias"))
|
||||
rename_keys.append((f"backbone/clip/text/transformer/resblocks.{i}/attn/query/kernel", f"owlv2.text_model.encoder.layers.{i}.self_attn.q_proj.weight"))
|
||||
rename_keys.append((f"backbone/clip/text/transformer/resblocks.{i}/attn/query/bias", f"owlv2.text_model.encoder.layers.{i}.self_attn.q_proj.bias"))
|
||||
rename_keys.append((f"backbone/clip/text/transformer/resblocks.{i}/attn/key/kernel", f"owlv2.text_model.encoder.layers.{i}.self_attn.k_proj.weight"))
|
||||
rename_keys.append((f"backbone/clip/text/transformer/resblocks.{i}/attn/key/bias", f"owlv2.text_model.encoder.layers.{i}.self_attn.k_proj.bias"))
|
||||
rename_keys.append((f"backbone/clip/text/transformer/resblocks.{i}/attn/value/kernel", f"owlv2.text_model.encoder.layers.{i}.self_attn.v_proj.weight"))
|
||||
rename_keys.append((f"backbone/clip/text/transformer/resblocks.{i}/attn/value/bias", f"owlv2.text_model.encoder.layers.{i}.self_attn.v_proj.bias"))
|
||||
rename_keys.append((f"backbone/clip/text/transformer/resblocks.{i}/attn/out/kernel", f"owlv2.text_model.encoder.layers.{i}.self_attn.out_proj.weight"))
|
||||
rename_keys.append((f"backbone/clip/text/transformer/resblocks.{i}/attn/out/bias", f"owlv2.text_model.encoder.layers.{i}.self_attn.out_proj.bias"))
|
||||
|
||||
rename_keys.append(("backbone/clip/text/ln_final/scale", "owlv2.text_model.final_layer_norm.weight"))
|
||||
rename_keys.append(("backbone/clip/text/ln_final/bias", "owlv2.text_model.final_layer_norm.bias"))
|
||||
|
||||
# logit scale
|
||||
rename_keys.append(("backbone/clip/logit_scale", "owlv2.logit_scale"))
|
||||
|
||||
# projection heads
|
||||
rename_keys.append(("backbone/clip/text/text_projection/kernel", "owlv2.text_projection.weight"))
|
||||
|
||||
# class and box heads
|
||||
rename_keys.append(("backbone/merged_class_token/scale", "layer_norm.weight"))
|
||||
rename_keys.append(("backbone/merged_class_token/bias", "layer_norm.bias"))
|
||||
rename_keys.append(("class_head/Dense_0/kernel", "class_head.dense0.weight"))
|
||||
rename_keys.append(("class_head/Dense_0/bias", "class_head.dense0.bias"))
|
||||
rename_keys.append(("class_head/logit_shift/kernel", "class_head.logit_shift.weight"))
|
||||
rename_keys.append(("class_head/logit_scale/kernel", "class_head.logit_scale.weight"))
|
||||
rename_keys.append(("class_head/logit_scale/bias", "class_head.logit_scale.bias"))
|
||||
rename_keys.append(("class_head/logit_shift/bias", "class_head.logit_shift.bias"))
|
||||
rename_keys.append(("obj_box_head/Dense_0/kernel", "box_head.dense0.weight"))
|
||||
rename_keys.append(("obj_box_head/Dense_0/bias", "box_head.dense0.bias"))
|
||||
rename_keys.append(("obj_box_head/Dense_1/kernel", "box_head.dense1.weight"))
|
||||
rename_keys.append(("obj_box_head/Dense_1/bias", "box_head.dense1.bias"))
|
||||
rename_keys.append(("obj_box_head/Dense_2/kernel", "box_head.dense2.weight"))
|
||||
rename_keys.append(("obj_box_head/Dense_2/bias", "box_head.dense2.bias"))
|
||||
|
||||
# objectness head (only for v2)
|
||||
if "v2" in model_name:
|
||||
rename_keys.append(("objectness_head/Dense_0/kernel", "objectness_head.dense0.weight"))
|
||||
rename_keys.append(("objectness_head/Dense_0/bias", "objectness_head.dense0.bias"))
|
||||
rename_keys.append(("objectness_head/Dense_1/kernel", "objectness_head.dense1.weight"))
|
||||
rename_keys.append(("objectness_head/Dense_1/bias", "objectness_head.dense1.bias"))
|
||||
rename_keys.append(("objectness_head/Dense_2/kernel", "objectness_head.dense2.weight"))
|
||||
rename_keys.append(("objectness_head/Dense_2/bias", "objectness_head.dense2.bias"))
|
||||
|
||||
# fmt: on
|
||||
|
||||
return rename_keys
|
||||
|
||||
|
||||
def rename_and_reshape_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" in new:
|
||||
print("Reshaping patch embedding... for", 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 new.endswith("bias"):
|
||||
val = val.reshape(-1)
|
||||
|
||||
dct[new] = torch.from_numpy(np.array(val))
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def convert_owlv2_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub, verify_logits):
|
||||
"""
|
||||
Copy/paste/tweak model's weights to our OWL-ViT structure.
|
||||
"""
|
||||
config = get_owlv2_config(model_name)
|
||||
|
||||
# see available checkpoints at https://github.com/google-research/scenic/tree/main/scenic/projects/owl_vit#pretrained-checkpoints
|
||||
variables = checkpoints.restore_checkpoint(checkpoint_path, target=None)
|
||||
variables = variables["params"] if "v2" in model_name else variables["optimizer"]["target"]
|
||||
flax_params = jax.tree_util.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, variables)
|
||||
state_dict = flatten_nested_dict(flax_params)
|
||||
|
||||
# Rename keys
|
||||
rename_keys = create_rename_keys(config, model_name)
|
||||
for src, dest in rename_keys:
|
||||
rename_and_reshape_key(state_dict, src, dest, config)
|
||||
|
||||
# load HuggingFace model
|
||||
model = Owlv2ForObjectDetection(config)
|
||||
missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
|
||||
assert missing_keys == ["owlv2.visual_projection.weight"]
|
||||
assert unexpected_keys == []
|
||||
model.eval()
|
||||
|
||||
# Initialize image processor
|
||||
size = {"height": config.vision_config.image_size, "width": config.vision_config.image_size}
|
||||
image_processor = Owlv2ImageProcessor(size=size)
|
||||
# Initialize tokenizer
|
||||
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32", pad_token="!", model_max_length=16)
|
||||
# Initialize processor
|
||||
processor = Owlv2Processor(image_processor=image_processor, tokenizer=tokenizer)
|
||||
|
||||
# Verify pixel_values and input_ids
|
||||
filepath = hf_hub_download(repo_id="nielsr/test-image", filename="owlvit_pixel_values_960.pt", repo_type="dataset")
|
||||
original_pixel_values = torch.load(filepath).permute(0, 3, 1, 2)
|
||||
|
||||
filepath = hf_hub_download(repo_id="nielsr/test-image", filename="owlv2_input_ids.pt", repo_type="dataset")
|
||||
original_input_ids = torch.load(filepath).squeeze()
|
||||
|
||||
filepath = hf_hub_download(repo_id="adirik/OWL-ViT", repo_type="space", filename="assets/astronaut.png")
|
||||
image = Image.open(filepath)
|
||||
texts = [["face", "rocket", "nasa badge", "star-spangled banner"]]
|
||||
inputs = processor(text=texts, images=image, return_tensors="pt")
|
||||
|
||||
if "large" not in model_name:
|
||||
assert torch.allclose(inputs.pixel_values, original_pixel_values.float(), atol=1e-6)
|
||||
assert torch.allclose(inputs.input_ids[:4, :], original_input_ids[:4, :], atol=1e-6)
|
||||
|
||||
with torch.no_grad():
|
||||
outputs = model(**inputs)
|
||||
logits = outputs.logits
|
||||
pred_boxes = outputs.pred_boxes
|
||||
objectness_logits = outputs.objectness_logits
|
||||
|
||||
if verify_logits:
|
||||
if model_name == "owlv2-base-patch16":
|
||||
expected_logits = torch.tensor(
|
||||
[[-10.0043, -9.0226, -8.0433], [-12.4569, -14.0380, -12.6153], [-21.0731, -22.2705, -21.8850]]
|
||||
)
|
||||
expected_boxes = torch.tensor(
|
||||
[[0.0136, 0.0223, 0.0269], [0.0406, 0.0327, 0.0797], [0.0638, 0.1539, 0.1255]]
|
||||
)
|
||||
expected_objectness_logits = torch.tensor(
|
||||
[[-5.6589, -7.7702, -16.3965]],
|
||||
)
|
||||
elif model_name == "owlv2-base-patch16-finetuned":
|
||||
expected_logits = torch.tensor(
|
||||
[[-9.2391, -9.2313, -8.0295], [-14.5498, -16.8450, -14.7166], [-15.1278, -17.3060, -15.7169]],
|
||||
)
|
||||
expected_boxes = torch.tensor(
|
||||
[[0.0103, 0.0094, 0.0207], [0.0483, 0.0729, 0.1013], [0.0629, 0.1396, 0.1313]]
|
||||
)
|
||||
expected_objectness_logits = torch.tensor(
|
||||
[[-6.5234, -13.3788, -14.6627]],
|
||||
)
|
||||
elif model_name == "owlv2-base-patch16-ensemble":
|
||||
expected_logits = torch.tensor(
|
||||
[[-8.6353, -9.5409, -6.6154], [-7.9442, -9.6151, -6.7117], [-12.4593, -15.3332, -12.1048]]
|
||||
)
|
||||
expected_boxes = torch.tensor(
|
||||
[[0.0126, 0.0090, 0.0238], [0.0387, 0.0227, 0.0754], [0.0582, 0.1058, 0.1139]]
|
||||
)
|
||||
expected_objectness_logits = torch.tensor(
|
||||
[[-6.0628, -5.9507, -10.4486]],
|
||||
)
|
||||
elif model_name == "owlv2-large-patch14":
|
||||
expected_logits = torch.tensor(
|
||||
[[-12.6662, -11.8384, -12.1880], [-16.0599, -16.5835, -16.9364], [-21.4957, -26.7038, -25.1313]],
|
||||
)
|
||||
expected_boxes = torch.tensor(
|
||||
[[0.0136, 0.0161, 0.0256], [0.0126, 0.0135, 0.0202], [0.0498, 0.0948, 0.0915]],
|
||||
)
|
||||
expected_objectness_logits = torch.tensor(
|
||||
[[-6.7196, -9.4590, -13.9472]],
|
||||
)
|
||||
elif model_name == "owlv2-large-patch14-finetuned":
|
||||
expected_logits = torch.tensor(
|
||||
[[-9.5413, -9.7130, -7.9762], [-9.5731, -9.7277, -8.2252], [-15.4434, -19.3084, -16.5490]],
|
||||
)
|
||||
expected_boxes = torch.tensor(
|
||||
[[0.0089, 0.0080, 0.0175], [0.0112, 0.0098, 0.0179], [0.0375, 0.0821, 0.0528]],
|
||||
)
|
||||
expected_objectness_logits = torch.tensor(
|
||||
[[-6.2655, -6.5845, -11.3105]],
|
||||
)
|
||||
elif model_name == "owlv2-large-patch14-ensemble":
|
||||
expected_logits = torch.tensor(
|
||||
[[-12.2037, -12.2070, -11.5371], [-13.4875, -13.8235, -13.1586], [-18.2007, -22.9834, -20.6816]],
|
||||
)
|
||||
expected_boxes = torch.tensor(
|
||||
[[0.0126, 0.0127, 0.0222], [0.0107, 0.0113, 0.0164], [0.0482, 0.1162, 0.0885]],
|
||||
)
|
||||
expected_objectness_logits = torch.tensor(
|
||||
[[-7.7572, -8.3637, -13.0334]],
|
||||
)
|
||||
|
||||
print("Objectness logits:", objectness_logits[:3, :3])
|
||||
print("Logits:", logits[0, :3, :3])
|
||||
print("Pred boxes:", pred_boxes[0, :3, :3])
|
||||
|
||||
assert torch.allclose(logits[0, :3, :3], expected_logits, atol=1e-3)
|
||||
assert torch.allclose(pred_boxes[0, :3, :3], expected_boxes, atol=1e-3)
|
||||
assert torch.allclose(objectness_logits[:3, :3], expected_objectness_logits, atol=1e-3)
|
||||
print("Looks ok!")
|
||||
else:
|
||||
print("Model converted without verifying logits")
|
||||
|
||||
if pytorch_dump_folder_path is not None:
|
||||
print("Saving model and processor locally...")
|
||||
# Create folder to save model
|
||||
if not os.path.isdir(pytorch_dump_folder_path):
|
||||
os.mkdir(pytorch_dump_folder_path)
|
||||
|
||||
model.save_pretrained(pytorch_dump_folder_path)
|
||||
processor.save_pretrained(pytorch_dump_folder_path)
|
||||
|
||||
if push_to_hub:
|
||||
print(f"Pushing {model_name} to the hub...")
|
||||
model.push_to_hub(f"google/{model_name}")
|
||||
processor.push_to_hub(f"google/{model_name}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
# Required parameters
|
||||
parser.add_argument(
|
||||
"--model_name",
|
||||
default="owlv2-base-patch16",
|
||||
choices=[
|
||||
"owlv2-base-patch16",
|
||||
"owlv2-base-patch16-finetuned",
|
||||
"owlv2-base-patch16-ensemble",
|
||||
"owlv2-large-patch14",
|
||||
"owlv2-large-patch14-finetuned",
|
||||
"owlv2-large-patch14-ensemble",
|
||||
],
|
||||
type=str,
|
||||
help="Name of the Owlv2 model you'd like to convert from FLAX to PyTorch.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--checkpoint_path",
|
||||
default=None,
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to the original Flax checkpoint.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--pytorch_dump_folder_path",
|
||||
default=None,
|
||||
type=str,
|
||||
required=False,
|
||||
help="Path to the output PyTorch model directory.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--verify_logits",
|
||||
action="store_false",
|
||||
required=False,
|
||||
help="Path to the output PyTorch model directory.",
|
||||
)
|
||||
parser.add_argument("--push_to_hub", action="store_true", help="Push model and image preprocessor to the hub")
|
||||
|
||||
args = parser.parse_args()
|
||||
convert_owlv2_checkpoint(
|
||||
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.verify_logits
|
||||
)
|
||||
596
src/transformers/models/owlv2/image_processing_owlv2.py
Normal file
596
src/transformers/models/owlv2/image_processing_owlv2.py
Normal file
@@ -0,0 +1,596 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2023 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 OWLv2."""
|
||||
|
||||
import warnings
|
||||
from typing import Dict, List, Optional, Tuple, Union
|
||||
|
||||
import numpy as np
|
||||
|
||||
from ...image_processing_utils import BaseImageProcessor, BatchFeature
|
||||
from ...image_transforms import (
|
||||
center_to_corners_format,
|
||||
pad,
|
||||
to_channel_dimension_format,
|
||||
)
|
||||
from ...image_utils import (
|
||||
OPENAI_CLIP_MEAN,
|
||||
OPENAI_CLIP_STD,
|
||||
ChannelDimension,
|
||||
ImageInput,
|
||||
PILImageResampling,
|
||||
get_image_size,
|
||||
infer_channel_dimension_format,
|
||||
is_scaled_image,
|
||||
make_list_of_images,
|
||||
to_numpy_array,
|
||||
valid_images,
|
||||
)
|
||||
from ...utils import (
|
||||
TensorType,
|
||||
is_scipy_available,
|
||||
is_torch_available,
|
||||
is_vision_available,
|
||||
logging,
|
||||
requires_backends,
|
||||
)
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
|
||||
if is_vision_available():
|
||||
import PIL
|
||||
|
||||
if is_scipy_available():
|
||||
from scipy import ndimage as ndi
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
# Copied from transformers.models.owlvit.image_processing_owlvit._upcast
|
||||
def _upcast(t):
|
||||
# Protects from numerical overflows in multiplications by upcasting to the equivalent higher type
|
||||
if t.is_floating_point():
|
||||
return t if t.dtype in (torch.float32, torch.float64) else t.float()
|
||||
else:
|
||||
return t if t.dtype in (torch.int32, torch.int64) else t.int()
|
||||
|
||||
|
||||
# Copied from transformers.models.owlvit.image_processing_owlvit.box_area
|
||||
def box_area(boxes):
|
||||
"""
|
||||
Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates.
|
||||
|
||||
Args:
|
||||
boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`):
|
||||
Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1
|
||||
< x2` and `0 <= y1 < y2`.
|
||||
Returns:
|
||||
`torch.FloatTensor`: a tensor containing the area for each box.
|
||||
"""
|
||||
boxes = _upcast(boxes)
|
||||
return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
|
||||
|
||||
|
||||
# Copied from transformers.models.owlvit.image_processing_owlvit.box_iou
|
||||
def box_iou(boxes1, boxes2):
|
||||
area1 = box_area(boxes1)
|
||||
area2 = box_area(boxes2)
|
||||
|
||||
left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
|
||||
right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]
|
||||
|
||||
width_height = (right_bottom - left_top).clamp(min=0) # [N,M,2]
|
||||
inter = width_height[:, :, 0] * width_height[:, :, 1] # [N,M]
|
||||
|
||||
union = area1[:, None] + area2 - inter
|
||||
|
||||
iou = inter / union
|
||||
return iou, union
|
||||
|
||||
|
||||
def _preprocess_resize_output_shape(image, output_shape):
|
||||
"""Validate resize output shape according to input image.
|
||||
|
||||
Args:
|
||||
image (`np.ndarray`):
|
||||
Image to be resized.
|
||||
output_shape (`iterable`):
|
||||
Size of the generated output image `(rows, cols[, ...][, dim])`. If `dim` is not provided, the number of
|
||||
channels is preserved.
|
||||
|
||||
Returns
|
||||
image (`np.ndarray):
|
||||
The input image, but with additional singleton dimensions appended in the case where `len(output_shape) >
|
||||
input.ndim`.
|
||||
output_shape (`Tuple`):
|
||||
The output shape converted to tuple.
|
||||
|
||||
Raises ------ ValueError:
|
||||
If output_shape length is smaller than the image number of dimensions.
|
||||
|
||||
Notes ----- The input image is reshaped if its number of dimensions is not equal to output_shape_length.
|
||||
|
||||
"""
|
||||
output_shape = tuple(output_shape)
|
||||
output_ndim = len(output_shape)
|
||||
input_shape = image.shape
|
||||
if output_ndim > image.ndim:
|
||||
# append dimensions to input_shape
|
||||
input_shape += (1,) * (output_ndim - image.ndim)
|
||||
image = np.reshape(image, input_shape)
|
||||
elif output_ndim == image.ndim - 1:
|
||||
# multichannel case: append shape of last axis
|
||||
output_shape = output_shape + (image.shape[-1],)
|
||||
elif output_ndim < image.ndim:
|
||||
raise ValueError("output_shape length cannot be smaller than the " "image number of dimensions")
|
||||
|
||||
return image, output_shape
|
||||
|
||||
|
||||
def _clip_warp_output(input_image, output_image):
|
||||
"""Clip output image to range of values of input image.
|
||||
|
||||
Note that this function modifies the values of *output_image* in-place.
|
||||
|
||||
Taken from:
|
||||
https://github.com/scikit-image/scikit-image/blob/b4b521d6f0a105aabeaa31699949f78453ca3511/skimage/transform/_warps.py#L640.
|
||||
|
||||
Args:
|
||||
input_image : ndarray
|
||||
Input image.
|
||||
output_image : ndarray
|
||||
Output image, which is modified in-place.
|
||||
"""
|
||||
min_val = np.min(input_image)
|
||||
if np.isnan(min_val):
|
||||
# NaNs detected, use NaN-safe min/max
|
||||
min_func = np.nanmin
|
||||
max_func = np.nanmax
|
||||
min_val = min_func(input_image)
|
||||
else:
|
||||
min_func = np.min
|
||||
max_func = np.max
|
||||
max_val = max_func(input_image)
|
||||
|
||||
output_image = np.clip(output_image, min_val, max_val)
|
||||
|
||||
return output_image
|
||||
|
||||
|
||||
class Owlv2ImageProcessor(BaseImageProcessor):
|
||||
r"""
|
||||
Constructs an OWLv2 image processor.
|
||||
|
||||
Args:
|
||||
do_rescale (`bool`, *optional*, defaults to `True`):
|
||||
Whether to rescale the image by the specified scale `rescale_factor`. Can be overriden by `do_rescale` in
|
||||
the `preprocess` method.
|
||||
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
|
||||
Scale factor to use if rescaling the image. Can be overriden by `rescale_factor` in the `preprocess`
|
||||
method.
|
||||
do_pad (`bool`, *optional*, defaults to `True`):
|
||||
Whether to pad the image to a square with gray pixels on the bottom and the right. Can be overriden by
|
||||
`do_pad` in the `preprocess` method.
|
||||
do_resize (`bool`, *optional*, defaults to `True`):
|
||||
Controls whether to resize the image's (height, width) dimensions to the specified `size`. Can be overriden
|
||||
by `do_resize` in the `preprocess` method.
|
||||
size (`Dict[str, int]` *optional*, defaults to `{"height": 960, "width": 960}`):
|
||||
Size to resize the image to. Can be overriden by `size` in the `preprocess` method.
|
||||
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
|
||||
Resampling method to use if resizing the image. Can be overriden by `resample` in the `preprocess` method.
|
||||
do_normalize (`bool`, *optional*, defaults to `True`):
|
||||
Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
|
||||
method.
|
||||
image_mean (`float` or `List[float]`, *optional*, defaults to `OPENAI_CLIP_MEAN`):
|
||||
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 `OPENAI_CLIP_STD`):
|
||||
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.
|
||||
"""
|
||||
|
||||
model_input_names = ["pixel_values"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
do_rescale: bool = True,
|
||||
rescale_factor: Union[int, float] = 1 / 255,
|
||||
do_pad: bool = True,
|
||||
do_resize: bool = True,
|
||||
size: Dict[str, int] = None,
|
||||
resample: PILImageResampling = PILImageResampling.BILINEAR,
|
||||
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)
|
||||
|
||||
self.do_rescale = do_rescale
|
||||
self.rescale_factor = rescale_factor
|
||||
self.do_pad = do_pad
|
||||
self.do_resize = do_resize
|
||||
self.size = size if size is not None else {"height": 960, "width": 960}
|
||||
self.resample = resample
|
||||
self.do_normalize = do_normalize
|
||||
self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
|
||||
self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
|
||||
|
||||
def pad(
|
||||
self,
|
||||
image: np.array,
|
||||
data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
):
|
||||
"""
|
||||
Pad an image to a square with gray pixels on the bottom and the right, as per the original OWLv2
|
||||
implementation.
|
||||
|
||||
Args:
|
||||
image (`np.ndarray`):
|
||||
Image to pad.
|
||||
data_format (`str` or `ChannelDimension`, *optional*):
|
||||
The channel dimension format of the image. If not provided, it will be the same as the input image.
|
||||
input_data_format (`ChannelDimension` or `str`, *optional*):
|
||||
The channel dimension format of the input image. If not provided, it will be inferred from the input
|
||||
image.
|
||||
"""
|
||||
height, width = get_image_size(image)
|
||||
size = max(height, width)
|
||||
image = pad(
|
||||
image=image,
|
||||
padding=((0, size - height), (0, size - width)),
|
||||
constant_values=0.5,
|
||||
data_format=data_format,
|
||||
input_data_format=input_data_format,
|
||||
)
|
||||
|
||||
return image
|
||||
|
||||
def resize(
|
||||
self,
|
||||
image: np.ndarray,
|
||||
size: Dict[str, int],
|
||||
anti_aliasing: bool = True,
|
||||
anti_aliasing_sigma=None,
|
||||
data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
||||
**kwargs,
|
||||
) -> np.ndarray:
|
||||
"""
|
||||
Resize an image as per the original implementation.
|
||||
|
||||
Args:
|
||||
image (`np.ndarray`):
|
||||
Image to resize.
|
||||
size (`Dict[str, int]`):
|
||||
Dictionary containing the height and width to resize the image to.
|
||||
anti_aliasing (`bool`, *optional*, defaults to `True`):
|
||||
Whether to apply anti-aliasing when downsampling the image.
|
||||
anti_aliasing_sigma (`float`, *optional*, defaults to `None`):
|
||||
Standard deviation for Gaussian kernel when downsampling the image. If `None`, it will be calculated
|
||||
automatically.
|
||||
data_format (`str` or `ChannelDimension`, *optional*):
|
||||
The channel dimension format of the image. If not provided, it will be the same as the input image.
|
||||
input_data_format (`ChannelDimension` or `str`, *optional*):
|
||||
The channel dimension format of the input image. If not provided, it will be inferred from the input
|
||||
image.
|
||||
"""
|
||||
requires_backends(self, "scipy")
|
||||
|
||||
output_shape = (size["height"], size["width"])
|
||||
image = to_channel_dimension_format(image, ChannelDimension.LAST)
|
||||
image, output_shape = _preprocess_resize_output_shape(image, output_shape)
|
||||
input_shape = image.shape
|
||||
factors = np.divide(input_shape, output_shape)
|
||||
|
||||
# Translate modes used by np.pad to those used by scipy.ndimage
|
||||
ndi_mode = "mirror"
|
||||
cval = 0
|
||||
order = 1
|
||||
if anti_aliasing:
|
||||
if anti_aliasing_sigma is None:
|
||||
anti_aliasing_sigma = np.maximum(0, (factors - 1) / 2)
|
||||
else:
|
||||
anti_aliasing_sigma = np.atleast_1d(anti_aliasing_sigma) * np.ones_like(factors)
|
||||
if np.any(anti_aliasing_sigma < 0):
|
||||
raise ValueError("Anti-aliasing standard deviation must be " "greater than or equal to zero")
|
||||
elif np.any((anti_aliasing_sigma > 0) & (factors <= 1)):
|
||||
warnings.warn(
|
||||
"Anti-aliasing standard deviation greater than zero but " "not down-sampling along all axes"
|
||||
)
|
||||
filtered = ndi.gaussian_filter(image, anti_aliasing_sigma, cval=cval, mode=ndi_mode)
|
||||
else:
|
||||
filtered = image
|
||||
|
||||
zoom_factors = [1 / f for f in factors]
|
||||
out = ndi.zoom(filtered, zoom_factors, order=order, mode=ndi_mode, cval=cval, grid_mode=True)
|
||||
|
||||
image = _clip_warp_output(image, out)
|
||||
|
||||
image = to_channel_dimension_format(image, input_data_format, ChannelDimension.LAST)
|
||||
image = (
|
||||
to_channel_dimension_format(image, data_format, input_data_format) if data_format is not None else image
|
||||
)
|
||||
return image
|
||||
|
||||
def preprocess(
|
||||
self,
|
||||
images: ImageInput,
|
||||
do_pad: bool = None,
|
||||
do_resize: bool = None,
|
||||
size: Dict[str, int] = 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: 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_pad (`bool`, *optional*, defaults to `self.do_pad`):
|
||||
Whether to pad the image to a square with gray pixels on the bottom and the right.
|
||||
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
|
||||
Whether to resize the image.
|
||||
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
|
||||
Size to resize the image to.
|
||||
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
|
||||
Whether to rescale the image values between [0 - 1].
|
||||
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
|
||||
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
|
||||
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.
|
||||
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
|
||||
Image standard deviation.
|
||||
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_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_pad = do_pad if do_pad is not None else self.do_pad
|
||||
do_resize = do_resize if do_resize is not None else self.do_resize
|
||||
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
|
||||
|
||||
size = size if size is not None else self.size
|
||||
|
||||
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.")
|
||||
|
||||
if do_normalize and (image_mean is None or image_std is None):
|
||||
raise ValueError("Image mean and std must be specified if do_normalize 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_rescale:
|
||||
images = [
|
||||
self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
|
||||
for image in images
|
||||
]
|
||||
|
||||
if do_pad:
|
||||
images = [self.pad(image=image, input_data_format=input_data_format) for image in images]
|
||||
|
||||
if do_resize:
|
||||
images = [
|
||||
self.resize(
|
||||
image=image,
|
||||
size=size,
|
||||
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)
|
||||
|
||||
# Copied from transformers.models.owlvit.image_processing_owlvit.OwlViTImageProcessor.post_process_object_detection
|
||||
def post_process_object_detection(
|
||||
self, outputs, threshold: float = 0.1, target_sizes: Union[TensorType, List[Tuple]] = None
|
||||
):
|
||||
"""
|
||||
Converts the raw output of [`OwlViTForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y,
|
||||
bottom_right_x, bottom_right_y) format.
|
||||
|
||||
Args:
|
||||
outputs ([`OwlViTObjectDetectionOutput`]):
|
||||
Raw outputs of the model.
|
||||
threshold (`float`, *optional*):
|
||||
Score threshold to keep object detection predictions.
|
||||
target_sizes (`torch.Tensor` or `List[Tuple[int, int]]`, *optional*):
|
||||
Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size
|
||||
`(height, width)` of each image in the batch. If unset, predictions will not be resized.
|
||||
Returns:
|
||||
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
|
||||
in the batch as predicted by the model.
|
||||
"""
|
||||
# TODO: (amy) add support for other frameworks
|
||||
logits, boxes = outputs.logits, outputs.pred_boxes
|
||||
|
||||
if target_sizes is not None:
|
||||
if len(logits) != len(target_sizes):
|
||||
raise ValueError(
|
||||
"Make sure that you pass in as many target sizes as the batch dimension of the logits"
|
||||
)
|
||||
|
||||
probs = torch.max(logits, dim=-1)
|
||||
scores = torch.sigmoid(probs.values)
|
||||
labels = probs.indices
|
||||
|
||||
# Convert to [x0, y0, x1, y1] format
|
||||
boxes = center_to_corners_format(boxes)
|
||||
|
||||
# Convert from relative [0, 1] to absolute [0, height] coordinates
|
||||
if target_sizes is not None:
|
||||
if isinstance(target_sizes, List):
|
||||
img_h = torch.Tensor([i[0] for i in target_sizes])
|
||||
img_w = torch.Tensor([i[1] for i in target_sizes])
|
||||
else:
|
||||
img_h, img_w = target_sizes.unbind(1)
|
||||
|
||||
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
|
||||
boxes = boxes * scale_fct[:, None, :]
|
||||
|
||||
results = []
|
||||
for s, l, b in zip(scores, labels, boxes):
|
||||
score = s[s > threshold]
|
||||
label = l[s > threshold]
|
||||
box = b[s > threshold]
|
||||
results.append({"scores": score, "labels": label, "boxes": box})
|
||||
|
||||
return results
|
||||
|
||||
# Copied from transformers.models.owlvit.image_processing_owlvit.OwlViTImageProcessor.post_process_image_guided_detection
|
||||
def post_process_image_guided_detection(self, outputs, threshold=0.0, nms_threshold=0.3, target_sizes=None):
|
||||
"""
|
||||
Converts the output of [`OwlViTForObjectDetection.image_guided_detection`] into the format expected by the COCO
|
||||
api.
|
||||
|
||||
Args:
|
||||
outputs ([`OwlViTImageGuidedObjectDetectionOutput`]):
|
||||
Raw outputs of the model.
|
||||
threshold (`float`, *optional*, defaults to 0.0):
|
||||
Minimum confidence threshold to use to filter out predicted boxes.
|
||||
nms_threshold (`float`, *optional*, defaults to 0.3):
|
||||
IoU threshold for non-maximum suppression of overlapping boxes.
|
||||
target_sizes (`torch.Tensor`, *optional*):
|
||||
Tensor of shape (batch_size, 2) where each entry is the (height, width) of the corresponding image in
|
||||
the batch. If set, predicted normalized bounding boxes are rescaled to the target sizes. If left to
|
||||
None, predictions will not be unnormalized.
|
||||
|
||||
Returns:
|
||||
`List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
|
||||
in the batch as predicted by the model. All labels are set to None as
|
||||
`OwlViTForObjectDetection.image_guided_detection` perform one-shot object detection.
|
||||
"""
|
||||
logits, target_boxes = outputs.logits, outputs.target_pred_boxes
|
||||
|
||||
if len(logits) != len(target_sizes):
|
||||
raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
|
||||
if target_sizes.shape[1] != 2:
|
||||
raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
|
||||
|
||||
probs = torch.max(logits, dim=-1)
|
||||
scores = torch.sigmoid(probs.values)
|
||||
|
||||
# Convert to [x0, y0, x1, y1] format
|
||||
target_boxes = center_to_corners_format(target_boxes)
|
||||
|
||||
# Apply non-maximum suppression (NMS)
|
||||
if nms_threshold < 1.0:
|
||||
for idx in range(target_boxes.shape[0]):
|
||||
for i in torch.argsort(-scores[idx]):
|
||||
if not scores[idx][i]:
|
||||
continue
|
||||
|
||||
ious = box_iou(target_boxes[idx][i, :].unsqueeze(0), target_boxes[idx])[0][0]
|
||||
ious[i] = -1.0 # Mask self-IoU.
|
||||
scores[idx][ious > nms_threshold] = 0.0
|
||||
|
||||
# Convert from relative [0, 1] to absolute [0, height] coordinates
|
||||
img_h, img_w = target_sizes.unbind(1)
|
||||
scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(target_boxes.device)
|
||||
target_boxes = target_boxes * scale_fct[:, None, :]
|
||||
|
||||
# Compute box display alphas based on prediction scores
|
||||
results = []
|
||||
alphas = torch.zeros_like(scores)
|
||||
|
||||
for idx in range(target_boxes.shape[0]):
|
||||
# Select scores for boxes matching the current query:
|
||||
query_scores = scores[idx]
|
||||
if not query_scores.nonzero().numel():
|
||||
continue
|
||||
|
||||
# Apply threshold on scores before scaling
|
||||
query_scores[query_scores < threshold] = 0.0
|
||||
|
||||
# Scale box alpha such that the best box for each query has alpha 1.0 and the worst box has alpha 0.1.
|
||||
# All other boxes will either belong to a different query, or will not be shown.
|
||||
max_score = torch.max(query_scores) + 1e-6
|
||||
query_alphas = (query_scores - (max_score * 0.1)) / (max_score * 0.9)
|
||||
query_alphas = torch.clip(query_alphas, 0.0, 1.0)
|
||||
alphas[idx] = query_alphas
|
||||
|
||||
mask = alphas[idx] > 0
|
||||
box_scores = alphas[idx][mask]
|
||||
boxes = target_boxes[idx][mask]
|
||||
results.append({"scores": box_scores, "labels": None, "boxes": boxes})
|
||||
|
||||
return results
|
||||
1786
src/transformers/models/owlv2/modeling_owlv2.py
Normal file
1786
src/transformers/models/owlv2/modeling_owlv2.py
Normal file
File diff suppressed because it is too large
Load Diff
190
src/transformers/models/owlv2/processing_owlv2.py
Normal file
190
src/transformers/models/owlv2/processing_owlv2.py
Normal file
@@ -0,0 +1,190 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2023 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 OWLv2
|
||||
"""
|
||||
|
||||
from typing import List
|
||||
|
||||
import numpy as np
|
||||
|
||||
from ...processing_utils import ProcessorMixin
|
||||
from ...tokenization_utils_base import BatchEncoding
|
||||
from ...utils import is_flax_available, is_tf_available, is_torch_available
|
||||
|
||||
|
||||
class Owlv2Processor(ProcessorMixin):
|
||||
r"""
|
||||
Constructs an Owlv2 processor which wraps [`Owlv2ImageProcessor`] and [`CLIPTokenizer`]/[`CLIPTokenizerFast`] into
|
||||
a single processor that interits both the image processor and tokenizer functionalities. See the
|
||||
[`~OwlViTProcessor.__call__`] and [`~OwlViTProcessor.decode`] for more information.
|
||||
|
||||
Args:
|
||||
image_processor ([`Owlv2ImageProcessor`]):
|
||||
The image processor is a required input.
|
||||
tokenizer ([`CLIPTokenizer`, `CLIPTokenizerFast`]):
|
||||
The tokenizer is a required input.
|
||||
"""
|
||||
attributes = ["image_processor", "tokenizer"]
|
||||
image_processor_class = "Owlv2ImageProcessor"
|
||||
tokenizer_class = ("CLIPTokenizer", "CLIPTokenizerFast")
|
||||
|
||||
def __init__(self, image_processor, tokenizer, **kwargs):
|
||||
super().__init__(image_processor, tokenizer)
|
||||
|
||||
# Copied from transformers.models.owlvit.processing_owlvit.OwlViTProcessor.__call__ with OWLViT->OWLv2
|
||||
def __call__(self, text=None, images=None, query_images=None, padding="max_length", return_tensors="np", **kwargs):
|
||||
"""
|
||||
Main method to prepare for the model one or several text(s) and image(s). This method forwards the `text` and
|
||||
`kwargs` arguments to CLIPTokenizerFast's [`~CLIPTokenizerFast.__call__`] if `text` is not `None` to encode:
|
||||
the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
|
||||
CLIPImageProcessor's [`~CLIPImageProcessor.__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.
|
||||
query_images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
|
||||
The query image to be prepared, one query image is expected per target image to be queried. 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.
|
||||
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:
|
||||
[`BatchEncoding`]: A [`BatchEncoding`] 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 query_images is None and images is None:
|
||||
raise ValueError(
|
||||
"You have to specify at least one text or query image or image. All three cannot be none."
|
||||
)
|
||||
|
||||
if text is not None:
|
||||
if isinstance(text, str) or (isinstance(text, List) and not isinstance(text[0], List)):
|
||||
encodings = [self.tokenizer(text, padding=padding, return_tensors=return_tensors, **kwargs)]
|
||||
|
||||
elif isinstance(text, List) and isinstance(text[0], List):
|
||||
encodings = []
|
||||
|
||||
# Maximum number of queries across batch
|
||||
max_num_queries = max([len(t) for t in text])
|
||||
|
||||
# Pad all batch samples to max number of text queries
|
||||
for t in text:
|
||||
if len(t) != max_num_queries:
|
||||
t = t + [" "] * (max_num_queries - len(t))
|
||||
|
||||
encoding = self.tokenizer(t, padding=padding, return_tensors=return_tensors, **kwargs)
|
||||
encodings.append(encoding)
|
||||
else:
|
||||
raise TypeError("Input text should be a string, a list of strings or a nested list of strings")
|
||||
|
||||
if return_tensors == "np":
|
||||
input_ids = np.concatenate([encoding["input_ids"] for encoding in encodings], axis=0)
|
||||
attention_mask = np.concatenate([encoding["attention_mask"] for encoding in encodings], axis=0)
|
||||
|
||||
elif return_tensors == "jax" and is_flax_available():
|
||||
import jax.numpy as jnp
|
||||
|
||||
input_ids = jnp.concatenate([encoding["input_ids"] for encoding in encodings], axis=0)
|
||||
attention_mask = jnp.concatenate([encoding["attention_mask"] for encoding in encodings], axis=0)
|
||||
|
||||
elif return_tensors == "pt" and is_torch_available():
|
||||
import torch
|
||||
|
||||
input_ids = torch.cat([encoding["input_ids"] for encoding in encodings], dim=0)
|
||||
attention_mask = torch.cat([encoding["attention_mask"] for encoding in encodings], dim=0)
|
||||
|
||||
elif return_tensors == "tf" and is_tf_available():
|
||||
import tensorflow as tf
|
||||
|
||||
input_ids = tf.stack([encoding["input_ids"] for encoding in encodings], axis=0)
|
||||
attention_mask = tf.stack([encoding["attention_mask"] for encoding in encodings], axis=0)
|
||||
|
||||
else:
|
||||
raise ValueError("Target return tensor type could not be returned")
|
||||
|
||||
encoding = BatchEncoding()
|
||||
encoding["input_ids"] = input_ids
|
||||
encoding["attention_mask"] = attention_mask
|
||||
|
||||
if query_images is not None:
|
||||
encoding = BatchEncoding()
|
||||
query_pixel_values = self.image_processor(
|
||||
query_images, return_tensors=return_tensors, **kwargs
|
||||
).pixel_values
|
||||
encoding["query_pixel_values"] = query_pixel_values
|
||||
|
||||
if images is not None:
|
||||
image_features = self.image_processor(images, return_tensors=return_tensors, **kwargs)
|
||||
|
||||
if text is not None and images is not None:
|
||||
encoding["pixel_values"] = image_features.pixel_values
|
||||
return encoding
|
||||
elif query_images is not None and images is not None:
|
||||
encoding["pixel_values"] = image_features.pixel_values
|
||||
return encoding
|
||||
elif text is not None or query_images is not None:
|
||||
return encoding
|
||||
else:
|
||||
return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)
|
||||
|
||||
# Copied from transformers.models.owlvit.processing_owlvit.OwlViTProcessor.post_process_object_detection with OWLViT->OWLv2
|
||||
def post_process_object_detection(self, *args, **kwargs):
|
||||
"""
|
||||
This method forwards all its arguments to [`OwlViTImageProcessor.post_process_object_detection`]. Please refer
|
||||
to the docstring of this method for more information.
|
||||
"""
|
||||
return self.image_processor.post_process_object_detection(*args, **kwargs)
|
||||
|
||||
# Copied from transformers.models.owlvit.processing_owlvit.OwlViTProcessor.post_process_image_guided_detection with OWLViT->OWLv2
|
||||
def post_process_image_guided_detection(self, *args, **kwargs):
|
||||
"""
|
||||
This method forwards all its arguments to [`OwlViTImageProcessor.post_process_one_shot_object_detection`].
|
||||
Please refer to the docstring of this method for more information.
|
||||
"""
|
||||
return self.image_processor.post_process_image_guided_detection(*args, **kwargs)
|
||||
|
||||
# Copied from transformers.models.owlvit.processing_owlvit.OwlViTProcessor.batch_decode
|
||||
def batch_decode(self, *args, **kwargs):
|
||||
"""
|
||||
This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
|
||||
refer to the docstring of this method for more information.
|
||||
"""
|
||||
return self.tokenizer.batch_decode(*args, **kwargs)
|
||||
|
||||
# Copied from transformers.models.owlvit.processing_owlvit.OwlViTProcessor.decode
|
||||
def decode(self, *args, **kwargs):
|
||||
"""
|
||||
This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
|
||||
the docstring of this method for more information.
|
||||
"""
|
||||
return self.tokenizer.decode(*args, **kwargs)
|
||||
@@ -66,15 +66,21 @@ class OwlViTTextConfig(PretrainedConfig):
|
||||
hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
|
||||
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-5):
|
||||
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
|
||||
The epsilon used by the layer normalization layers.
|
||||
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||
The dropout ratio for the attention probabilities.
|
||||
initializer_range (`float`, *optional*, defaults to 0.02):
|
||||
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||||
initializer_factor (`float`, *optional*, defaults to 1):
|
||||
initializer_factor (`float`, *optional*, defaults to 1.0):
|
||||
A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
|
||||
testing).
|
||||
pad_token_id (`int`, *optional*, defaults to 0):
|
||||
The id of the padding token in the input sequences.
|
||||
bos_token_id (`int`, *optional*, defaults to 49406):
|
||||
The id of the beginning-of-sequence token in the input sequences.
|
||||
eos_token_id (`int`, *optional*, defaults to 49407):
|
||||
The id of the end-of-sequence token in the input sequences.
|
||||
|
||||
Example:
|
||||
|
||||
@@ -268,6 +274,8 @@ class OwlViTConfig(PretrainedConfig):
|
||||
logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
|
||||
The inital value of the *logit_scale* parameter. Default is used as per the original OWL-ViT
|
||||
implementation.
|
||||
return_dict (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not the model should return a dictionary. If `False`, returns a tuple.
|
||||
kwargs (*optional*):
|
||||
Dictionary of keyword arguments.
|
||||
"""
|
||||
|
||||
@@ -582,11 +582,16 @@ class OwlViTPreTrainedModel(PreTrainedModel):
|
||||
|
||||
|
||||
OWLVIT_START_DOCSTRING = r"""
|
||||
|
||||
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
||||
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
||||
etc.)
|
||||
|
||||
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
||||
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
||||
and behavior.
|
||||
|
||||
Parameters:
|
||||
This model is a PyTorch [torch.nn.Module](https:
|
||||
//pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
|
||||
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
|
||||
behavior.
|
||||
config ([`OwlViTConfig`]): Model configuration class with all the parameters of the model.
|
||||
Initializing with a config file does not load the weights associated with the model, only the
|
||||
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
||||
@@ -1250,14 +1255,14 @@ class OwlViTModel(OwlViTPreTrainedModel):
|
||||
|
||||
|
||||
class OwlViTBoxPredictionHead(nn.Module):
|
||||
def __init__(self, config: OwlViTConfig):
|
||||
def __init__(self, config: OwlViTConfig, out_dim: int = 4):
|
||||
super().__init__()
|
||||
|
||||
width = config.vision_config.hidden_size
|
||||
self.dense0 = nn.Linear(width, width)
|
||||
self.dense1 = nn.Linear(width, width)
|
||||
self.gelu = nn.GELU()
|
||||
self.dense2 = nn.Linear(width, 4)
|
||||
self.dense2 = nn.Linear(width, out_dim)
|
||||
|
||||
def forward(self, image_features: torch.Tensor) -> torch.FloatTensor:
|
||||
output = self.dense0(image_features)
|
||||
|
||||
@@ -5860,6 +5860,44 @@ class OPTPreTrainedModel(metaclass=DummyObject):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
OWLV2_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
||||
|
||||
|
||||
class Owlv2ForObjectDetection(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class Owlv2Model(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class Owlv2PreTrainedModel(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class Owlv2TextModel(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class Owlv2VisionModel(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
||||
|
||||
|
||||
|
||||
@@ -373,6 +373,13 @@ class OneFormerImageProcessor(metaclass=DummyObject):
|
||||
requires_backends(self, ["vision"])
|
||||
|
||||
|
||||
class Owlv2ImageProcessor(metaclass=DummyObject):
|
||||
_backends = ["vision"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["vision"])
|
||||
|
||||
|
||||
class OwlViTFeatureExtractor(metaclass=DummyObject):
|
||||
_backends = ["vision"]
|
||||
|
||||
|
||||
0
tests/models/owlv2/__init__.py
Normal file
0
tests/models/owlv2/__init__.py
Normal file
125
tests/models/owlv2/test_image_processor_owlv2.py
Normal file
125
tests/models/owlv2/test_image_processor_owlv2.py
Normal file
@@ -0,0 +1,125 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2023 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, slow
|
||||
from transformers.utils import is_vision_available
|
||||
|
||||
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
|
||||
|
||||
|
||||
if is_vision_available():
|
||||
from PIL import Image
|
||||
|
||||
from transformers import Owlv2ImageProcessor
|
||||
|
||||
|
||||
class Owlv2ImageProcessingTester(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_normalize=True,
|
||||
image_mean=[0.48145466, 0.4578275, 0.40821073],
|
||||
image_std=[0.26862954, 0.26130258, 0.27577711],
|
||||
do_convert_rgb=True,
|
||||
):
|
||||
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 if size is not None else {"height": 18, "width": 18}
|
||||
self.do_normalize = do_normalize
|
||||
self.image_mean = image_mean
|
||||
self.image_std = image_std
|
||||
self.do_convert_rgb = do_convert_rgb
|
||||
|
||||
def prepare_image_processor_dict(self):
|
||||
return {
|
||||
"do_resize": self.do_resize,
|
||||
"size": self.size,
|
||||
"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
|
||||
class Owlv2ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
|
||||
image_processing_class = Owlv2ImageProcessor if is_vision_available() else None
|
||||
|
||||
def setUp(self):
|
||||
self.image_processor_tester = Owlv2ImageProcessingTester(self)
|
||||
|
||||
@property
|
||||
def image_processor_dict(self):
|
||||
return self.image_processor_tester.prepare_image_processor_dict()
|
||||
|
||||
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, "do_normalize"))
|
||||
self.assertTrue(hasattr(image_processing, "image_mean"))
|
||||
self.assertTrue(hasattr(image_processing, "image_std"))
|
||||
|
||||
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": 42, "width": 42}
|
||||
)
|
||||
self.assertEqual(image_processor.size, {"height": 42, "width": 42})
|
||||
|
||||
@slow
|
||||
def test_image_processor_integration_test(self):
|
||||
processor = Owlv2ImageProcessor()
|
||||
|
||||
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
|
||||
pixel_values = processor(image, return_tensors="pt").pixel_values
|
||||
|
||||
mean_value = round(pixel_values.mean().item(), 4)
|
||||
self.assertEqual(mean_value, 0.2353)
|
||||
|
||||
@unittest.skip("OWLv2 doesn't treat 4 channel PIL and numpy consistently yet") # FIXME Amy
|
||||
def test_call_numpy_4_channels(self):
|
||||
pass
|
||||
858
tests/models/owlv2/test_modeling_owlv2.py
Normal file
858
tests/models/owlv2/test_modeling_owlv2.py
Normal file
@@ -0,0 +1,858 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2023 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 Owlv2 model. """
|
||||
|
||||
|
||||
import inspect
|
||||
import os
|
||||
import tempfile
|
||||
import unittest
|
||||
|
||||
import numpy as np
|
||||
import requests
|
||||
|
||||
from transformers import Owlv2Config, Owlv2TextConfig, Owlv2VisionConfig
|
||||
from transformers.testing_utils import require_torch, require_torch_gpu, 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 Owlv2ForObjectDetection, Owlv2Model, Owlv2TextModel, Owlv2VisionModel
|
||||
from transformers.models.owlv2.modeling_owlv2 import OWLV2_PRETRAINED_MODEL_ARCHIVE_LIST
|
||||
|
||||
|
||||
if is_vision_available():
|
||||
from PIL import Image
|
||||
|
||||
from transformers import OwlViTProcessor
|
||||
|
||||
|
||||
# Copied from tests.models.owlvit.test_modeling_owlvit.OwlViTVisionModelTester with OwlViT->Owlv2
|
||||
class Owlv2VisionModelTester:
|
||||
def __init__(
|
||||
self,
|
||||
parent,
|
||||
batch_size=12,
|
||||
image_size=32,
|
||||
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 + 1 (we add 1 for the [CLS] token)
|
||||
num_patches = (image_size // patch_size) ** 2
|
||||
self.seq_length = num_patches + 1
|
||||
|
||||
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 Owlv2VisionConfig(
|
||||
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 = Owlv2VisionModel(config=config).to(torch_device)
|
||||
model.eval()
|
||||
|
||||
pixel_values = pixel_values.to(torch.float32)
|
||||
|
||||
with torch.no_grad():
|
||||
result = model(pixel_values)
|
||||
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
|
||||
num_patches = (self.image_size // self.patch_size) ** 2
|
||||
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
|
||||
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
|
||||
|
||||
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
|
||||
# Copied from tests.models.owlvit.test_modeling_owlvit.OwlViTVisionModelTest with OwlViT->Owlv2, OWL-ViT->OwlV2, OWLVIT->OWLV2
|
||||
class Owlv2VisionModelTest(ModelTesterMixin, unittest.TestCase):
|
||||
"""
|
||||
Here we also overwrite some of the tests of test_modeling_common.py, as OWLV2 does not use input_ids,
|
||||
inputs_embeds, attention_mask and seq_length.
|
||||
"""
|
||||
|
||||
all_model_classes = (Owlv2VisionModel,) 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 = Owlv2VisionModelTester(self)
|
||||
self.config_tester = ConfigTester(
|
||||
self, config_class=Owlv2VisionConfig, has_text_modality=False, hidden_size=37
|
||||
)
|
||||
|
||||
def test_config(self):
|
||||
self.config_tester.run_common_tests()
|
||||
|
||||
@unittest.skip(reason="OWLV2 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="OwlV2 does not support training yet")
|
||||
def test_training(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="OwlV2 does not support training yet")
|
||||
def test_training_gradient_checkpointing(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Owlv2VisionModel has no base class and is not available in MODEL_MAPPING")
|
||||
def test_save_load_fast_init_from_base(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Owlv2VisionModel has no base class and is not available in MODEL_MAPPING")
|
||||
def test_save_load_fast_init_to_base(self):
|
||||
pass
|
||||
|
||||
@slow
|
||||
def test_model_from_pretrained(self):
|
||||
for model_name in OWLV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
||||
model = Owlv2VisionModel.from_pretrained(model_name)
|
||||
self.assertIsNotNone(model)
|
||||
|
||||
|
||||
# Copied from tests.models.owlvit.test_modeling_owlvit.OwlViTTextModelTester with OwlViT->Owlv2
|
||||
class Owlv2TextModelTester:
|
||||
def __init__(
|
||||
self,
|
||||
parent,
|
||||
batch_size=12,
|
||||
num_queries=4,
|
||||
seq_length=16,
|
||||
is_training=True,
|
||||
use_input_mask=True,
|
||||
use_labels=True,
|
||||
vocab_size=99,
|
||||
hidden_size=64,
|
||||
num_hidden_layers=12,
|
||||
num_attention_heads=4,
|
||||
intermediate_size=37,
|
||||
dropout=0.1,
|
||||
attention_dropout=0.1,
|
||||
max_position_embeddings=16,
|
||||
initializer_range=0.02,
|
||||
scope=None,
|
||||
):
|
||||
self.parent = parent
|
||||
self.batch_size = batch_size
|
||||
self.num_queries = num_queries
|
||||
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
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
input_ids = ids_tensor([self.batch_size * self.num_queries, self.seq_length], self.vocab_size)
|
||||
input_mask = None
|
||||
|
||||
if self.use_input_mask:
|
||||
input_mask = random_attention_mask([self.batch_size * self.num_queries, self.seq_length])
|
||||
|
||||
if input_mask is not None:
|
||||
num_text, seq_length = input_mask.shape
|
||||
|
||||
rnd_start_indices = np.random.randint(1, seq_length - 1, size=(num_text,))
|
||||
for idx, start_index in enumerate(rnd_start_indices):
|
||||
input_mask[idx, :start_index] = 1
|
||||
input_mask[idx, start_index:] = 0
|
||||
|
||||
config = self.get_config()
|
||||
|
||||
return config, input_ids, input_mask
|
||||
|
||||
def get_config(self):
|
||||
return Owlv2TextConfig(
|
||||
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 = Owlv2TextModel(config=config).to(torch_device)
|
||||
model.eval()
|
||||
with torch.no_grad():
|
||||
result = model(input_ids=input_ids, attention_mask=input_mask)
|
||||
|
||||
self.parent.assertEqual(
|
||||
result.last_hidden_state.shape, (self.batch_size * self.num_queries, self.seq_length, self.hidden_size)
|
||||
)
|
||||
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size * self.num_queries, self.hidden_size))
|
||||
|
||||
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
|
||||
# Copied from tests.models.owlvit.test_modeling_owlvit.OwlViTTextModelTest with OwlViT->Owlv2, OWL-ViT->OwlV2, OWLVIT->OWLV2
|
||||
class Owlv2TextModelTest(ModelTesterMixin, unittest.TestCase):
|
||||
all_model_classes = (Owlv2TextModel,) if is_torch_available() else ()
|
||||
fx_compatible = False
|
||||
test_pruning = False
|
||||
test_head_masking = False
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = Owlv2TextModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=Owlv2TextConfig, hidden_size=37)
|
||||
|
||||
def test_config(self):
|
||||
self.config_tester.run_common_tests()
|
||||
|
||||
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="OwlV2 does not support training yet")
|
||||
def test_training(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="OwlV2 does not support training yet")
|
||||
def test_training_gradient_checkpointing(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="OWLV2 does not use inputs_embeds")
|
||||
def test_inputs_embeds(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Owlv2TextModel has no base class and is not available in MODEL_MAPPING")
|
||||
def test_save_load_fast_init_from_base(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Owlv2TextModel has no base class and is not available in MODEL_MAPPING")
|
||||
def test_save_load_fast_init_to_base(self):
|
||||
pass
|
||||
|
||||
@slow
|
||||
def test_model_from_pretrained(self):
|
||||
for model_name in OWLV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
||||
model = Owlv2TextModel.from_pretrained(model_name)
|
||||
self.assertIsNotNone(model)
|
||||
|
||||
|
||||
class Owlv2ModelTester:
|
||||
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 = Owlv2TextModelTester(parent, **text_kwargs)
|
||||
self.vision_model_tester = Owlv2VisionModelTester(parent, **vision_kwargs)
|
||||
self.is_training = is_training
|
||||
self.text_config = self.text_model_tester.get_config().to_dict()
|
||||
self.vision_config = self.vision_model_tester.get_config().to_dict()
|
||||
|
||||
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 Owlv2Config.from_text_vision_configs(self.text_config, self.vision_config, projection_dim=64)
|
||||
|
||||
def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
|
||||
model = Owlv2Model(config).to(torch_device).eval()
|
||||
|
||||
with torch.no_grad():
|
||||
result = model(
|
||||
input_ids=input_ids,
|
||||
pixel_values=pixel_values,
|
||||
attention_mask=attention_mask,
|
||||
)
|
||||
|
||||
image_logits_size = (
|
||||
self.vision_model_tester.batch_size,
|
||||
self.text_model_tester.batch_size * self.text_model_tester.num_queries,
|
||||
)
|
||||
text_logits_size = (
|
||||
self.text_model_tester.batch_size * self.text_model_tester.num_queries,
|
||||
self.vision_model_tester.batch_size,
|
||||
)
|
||||
self.parent.assertEqual(result.logits_per_image.shape, image_logits_size)
|
||||
self.parent.assertEqual(result.logits_per_text.shape, text_logits_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 = {
|
||||
"pixel_values": pixel_values,
|
||||
"input_ids": input_ids,
|
||||
"attention_mask": attention_mask,
|
||||
"return_loss": False,
|
||||
}
|
||||
return config, inputs_dict
|
||||
|
||||
|
||||
@require_torch
|
||||
# Copied from tests.models.owlvit.test_modeling_owlvit.OwlViTModelTest with OwlViT->Owlv2, OWL-ViT->OwlV2, OWLVIT->OWLV2
|
||||
class Owlv2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
||||
all_model_classes = (Owlv2Model,) if is_torch_available() else ()
|
||||
pipeline_model_mapping = (
|
||||
{"feature-extraction": Owlv2Model, "zero-shot-object-detection": Owlv2ForObjectDetection}
|
||||
if is_torch_available()
|
||||
else {}
|
||||
)
|
||||
fx_compatible = False
|
||||
test_head_masking = False
|
||||
test_pruning = False
|
||||
test_resize_embeddings = False
|
||||
test_attention_outputs = False
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = Owlv2ModelTester(self)
|
||||
|
||||
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")
|
||||
def test_hidden_states_output(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Inputs_embeds is tested in individual model tests")
|
||||
def test_inputs_embeds(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Retain_grad is tested in individual model tests")
|
||||
def test_retain_grad_hidden_states_attentions(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Owlv2Model does not have input/output embeddings")
|
||||
def test_model_common_attributes(self):
|
||||
pass
|
||||
|
||||
# override as the `logit_scale` parameter initilization is different for OWLV2
|
||||
def test_initialization(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
configs_no_init = _config_zero_init(config)
|
||||
for model_class in self.all_model_classes:
|
||||
model = model_class(config=configs_no_init)
|
||||
for name, param in model.named_parameters():
|
||||
if param.requires_grad:
|
||||
# check if `logit_scale` is initilized as per the original implementation
|
||||
if name == "logit_scale":
|
||||
self.assertAlmostEqual(
|
||||
param.data.item(),
|
||||
np.log(1 / 0.07),
|
||||
delta=1e-3,
|
||||
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
||||
)
|
||||
else:
|
||||
self.assertIn(
|
||||
((param.data.mean() * 1e9).round() / 1e9).item(),
|
||||
[0.0, 1.0],
|
||||
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
||||
)
|
||||
|
||||
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).to(torch_device)
|
||||
model.eval()
|
||||
|
||||
try:
|
||||
input_ids = inputs_dict["input_ids"]
|
||||
pixel_values = inputs_dict["pixel_values"] # OWLV2 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.")
|
||||
|
||||
loaded_model = 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)
|
||||
|
||||
def test_load_vision_text_config(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
# Save Owlv2Config and check if we can load Owlv2VisionConfig from it
|
||||
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
||||
config.save_pretrained(tmp_dir_name)
|
||||
vision_config = Owlv2VisionConfig.from_pretrained(tmp_dir_name)
|
||||
self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
|
||||
|
||||
# Save Owlv2Config and check if we can load Owlv2TextConfig from it
|
||||
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
||||
config.save_pretrained(tmp_dir_name)
|
||||
text_config = Owlv2TextConfig.from_pretrained(tmp_dir_name)
|
||||
self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())
|
||||
|
||||
@slow
|
||||
def test_model_from_pretrained(self):
|
||||
for model_name in OWLV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
||||
model = Owlv2Model.from_pretrained(model_name)
|
||||
self.assertIsNotNone(model)
|
||||
|
||||
|
||||
# Copied from tests.models.owlvit.test_modeling_owlvit.OwlViTForObjectDetectionTester with OwlViT->Owlv2, OWL-ViT->OwlV2, OWLVIT->OWLV2
|
||||
class Owlv2ForObjectDetectionTester:
|
||||
def __init__(self, parent, is_training=True):
|
||||
self.parent = parent
|
||||
self.text_model_tester = Owlv2TextModelTester(parent)
|
||||
self.vision_model_tester = Owlv2VisionModelTester(parent)
|
||||
self.is_training = is_training
|
||||
self.text_config = self.text_model_tester.get_config().to_dict()
|
||||
self.vision_config = self.vision_model_tester.get_config().to_dict()
|
||||
|
||||
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, pixel_values, input_ids, attention_mask
|
||||
|
||||
def get_config(self):
|
||||
return Owlv2Config.from_text_vision_configs(self.text_config, self.vision_config, projection_dim=64)
|
||||
|
||||
def create_and_check_model(self, config, pixel_values, input_ids, attention_mask):
|
||||
model = Owlv2ForObjectDetection(config).to(torch_device).eval()
|
||||
with torch.no_grad():
|
||||
result = model(
|
||||
pixel_values=pixel_values,
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
return_dict=True,
|
||||
)
|
||||
|
||||
pred_boxes_size = (
|
||||
self.vision_model_tester.batch_size,
|
||||
(self.vision_model_tester.image_size // self.vision_model_tester.patch_size) ** 2,
|
||||
4,
|
||||
)
|
||||
pred_logits_size = (
|
||||
self.vision_model_tester.batch_size,
|
||||
(self.vision_model_tester.image_size // self.vision_model_tester.patch_size) ** 2,
|
||||
4,
|
||||
)
|
||||
pred_class_embeds_size = (
|
||||
self.vision_model_tester.batch_size,
|
||||
(self.vision_model_tester.image_size // self.vision_model_tester.patch_size) ** 2,
|
||||
self.text_model_tester.hidden_size,
|
||||
)
|
||||
self.parent.assertEqual(result.pred_boxes.shape, pred_boxes_size)
|
||||
self.parent.assertEqual(result.logits.shape, pred_logits_size)
|
||||
self.parent.assertEqual(result.class_embeds.shape, pred_class_embeds_size)
|
||||
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config_and_inputs = self.prepare_config_and_inputs()
|
||||
config, pixel_values, input_ids, attention_mask = config_and_inputs
|
||||
inputs_dict = {
|
||||
"pixel_values": pixel_values,
|
||||
"input_ids": input_ids,
|
||||
"attention_mask": attention_mask,
|
||||
}
|
||||
return config, inputs_dict
|
||||
|
||||
|
||||
@require_torch
|
||||
# Copied from tests.models.owlvit.test_modeling_owlvit.OwlViTForObjectDetectionTest with OwlViT->Owlv2, OWL-ViT->OwlV2, OWLVIT->OWLV2
|
||||
class Owlv2ForObjectDetectionTest(ModelTesterMixin, unittest.TestCase):
|
||||
all_model_classes = (Owlv2ForObjectDetection,) if is_torch_available() else ()
|
||||
fx_compatible = False
|
||||
test_head_masking = False
|
||||
test_pruning = False
|
||||
test_resize_embeddings = False
|
||||
test_attention_outputs = False
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = Owlv2ForObjectDetectionTester(self)
|
||||
|
||||
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")
|
||||
def test_hidden_states_output(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Inputs_embeds is tested in individual model tests")
|
||||
def test_inputs_embeds(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Retain_grad is tested in individual model tests")
|
||||
def test_retain_grad_hidden_states_attentions(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Owlv2Model does not have input/output embeddings")
|
||||
def test_model_common_attributes(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Test_initialization is tested in individual model tests")
|
||||
def test_initialization(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Test_forward_signature is tested in individual model tests")
|
||||
def test_forward_signature(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Test_save_load_fast_init_from_base is tested in individual model tests")
|
||||
def test_save_load_fast_init_from_base(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="OwlV2 does not support training yet")
|
||||
def test_training(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="OwlV2 does not support training yet")
|
||||
def test_training_gradient_checkpointing(self):
|
||||
pass
|
||||
|
||||
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).to(torch_device)
|
||||
model.eval()
|
||||
|
||||
try:
|
||||
input_ids = inputs_dict["input_ids"]
|
||||
pixel_values = inputs_dict["pixel_values"] # OWLV2 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.")
|
||||
|
||||
loaded_model = 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)
|
||||
|
||||
@slow
|
||||
def test_model_from_pretrained(self):
|
||||
for model_name in OWLV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
||||
model = Owlv2ForObjectDetection.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"
|
||||
im = Image.open(requests.get(url, stream=True).raw)
|
||||
return im
|
||||
|
||||
|
||||
@require_vision
|
||||
@require_torch
|
||||
class Owlv2ModelIntegrationTest(unittest.TestCase):
|
||||
@slow
|
||||
def test_inference(self):
|
||||
model_name = "google/owlv2-base-patch16"
|
||||
model = Owlv2Model.from_pretrained(model_name).to(torch_device)
|
||||
processor = OwlViTProcessor.from_pretrained(model_name)
|
||||
|
||||
image = prepare_img()
|
||||
inputs = processor(
|
||||
text=[["a photo of a cat", "a photo of a dog"]],
|
||||
images=image,
|
||||
max_length=16,
|
||||
padding="max_length",
|
||||
return_tensors="pt",
|
||||
).to(torch_device)
|
||||
|
||||
# forward pass
|
||||
with torch.no_grad():
|
||||
outputs = model(**inputs)
|
||||
|
||||
# verify the logits
|
||||
self.assertEqual(
|
||||
outputs.logits_per_image.shape,
|
||||
torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
|
||||
)
|
||||
self.assertEqual(
|
||||
outputs.logits_per_text.shape,
|
||||
torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
|
||||
)
|
||||
expected_logits = torch.tensor([[-6.2229, -8.2601]], device=torch_device)
|
||||
self.assertTrue(torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3))
|
||||
|
||||
@slow
|
||||
def test_inference_object_detection(self):
|
||||
model_name = "google/owlv2-base-patch16"
|
||||
model = Owlv2ForObjectDetection.from_pretrained(model_name).to(torch_device)
|
||||
|
||||
processor = OwlViTProcessor.from_pretrained(model_name)
|
||||
|
||||
image = prepare_img()
|
||||
inputs = processor(
|
||||
text=[["a photo of a cat", "a photo of a dog"]],
|
||||
images=image,
|
||||
max_length=16,
|
||||
padding="max_length",
|
||||
return_tensors="pt",
|
||||
).to(torch_device)
|
||||
|
||||
with torch.no_grad():
|
||||
outputs = model(**inputs)
|
||||
|
||||
num_queries = int((model.config.vision_config.image_size / model.config.vision_config.patch_size) ** 2)
|
||||
self.assertEqual(outputs.pred_boxes.shape, torch.Size((1, num_queries, 4)))
|
||||
|
||||
expected_slice_logits = torch.tensor([[-21.4139, -21.6130], [-19.0084, -19.5491], [-20.9592, -21.3830]])
|
||||
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice_logits, atol=1e-4))
|
||||
expected_slice_boxes = torch.tensor(
|
||||
[[0.2413, 0.0519, 0.4533], [0.1395, 0.0457, 0.2507], [0.2330, 0.0505, 0.4277]],
|
||||
).to(torch_device)
|
||||
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4))
|
||||
|
||||
@slow
|
||||
def test_inference_one_shot_object_detection(self):
|
||||
model_name = "google/owlv2-base-patch16"
|
||||
model = Owlv2ForObjectDetection.from_pretrained(model_name).to(torch_device)
|
||||
|
||||
processor = OwlViTProcessor.from_pretrained(model_name)
|
||||
|
||||
image = prepare_img()
|
||||
query_image = prepare_img()
|
||||
inputs = processor(
|
||||
images=image,
|
||||
query_images=query_image,
|
||||
max_length=16,
|
||||
padding="max_length",
|
||||
return_tensors="pt",
|
||||
).to(torch_device)
|
||||
|
||||
with torch.no_grad():
|
||||
outputs = model.image_guided_detection(**inputs)
|
||||
|
||||
num_queries = int((model.config.vision_config.image_size / model.config.vision_config.patch_size) ** 2)
|
||||
self.assertEqual(outputs.target_pred_boxes.shape, torch.Size((1, num_queries, 4)))
|
||||
|
||||
expected_slice_boxes = torch.tensor(
|
||||
[[0.2413, 0.0519, 0.4533], [0.1395, 0.0457, 0.2507], [0.2330, 0.0505, 0.4277]],
|
||||
).to(torch_device)
|
||||
self.assertTrue(torch.allclose(outputs.target_pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4))
|
||||
|
||||
@slow
|
||||
@require_torch_gpu
|
||||
def test_inference_one_shot_object_detection_fp16(self):
|
||||
model_name = "google/owlv2-base-patch16"
|
||||
model = Owlv2ForObjectDetection.from_pretrained(model_name, torch_dtype=torch.float16).to(torch_device)
|
||||
|
||||
processor = OwlViTProcessor.from_pretrained(model_name)
|
||||
|
||||
image = prepare_img()
|
||||
query_image = prepare_img()
|
||||
inputs = processor(
|
||||
images=image,
|
||||
query_images=query_image,
|
||||
max_length=16,
|
||||
padding="max_length",
|
||||
return_tensors="pt",
|
||||
).to(torch_device)
|
||||
|
||||
with torch.no_grad():
|
||||
outputs = model.image_guided_detection(**inputs)
|
||||
|
||||
# No need to check the logits, we just check inference runs fine.
|
||||
num_queries = int((model.config.vision_config.image_size / model.config.vision_config.patch_size) ** 2)
|
||||
self.assertEqual(outputs.target_pred_boxes.shape, torch.Size((1, num_queries, 4)))
|
||||
@@ -417,9 +417,6 @@ OBJECTS_TO_IGNORE = [
|
||||
"OneFormerProcessor",
|
||||
"OpenAIGPTTokenizerFast",
|
||||
"OpenLlamaConfig",
|
||||
"OwlViTConfig",
|
||||
"OwlViTModel",
|
||||
"OwlViTTextConfig",
|
||||
"PLBartConfig",
|
||||
"PegasusConfig",
|
||||
"PegasusTokenizer",
|
||||
|
||||
@@ -234,6 +234,8 @@ IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
|
||||
"OpenAIGPTDoubleHeadsModel",
|
||||
"OwlViTTextModel",
|
||||
"OwlViTVisionModel",
|
||||
"Owlv2TextModel",
|
||||
"Owlv2VisionModel",
|
||||
"OwlViTForObjectDetection",
|
||||
"RagModel",
|
||||
"RagSequenceForGeneration",
|
||||
|
||||
Reference in New Issue
Block a user